Industry Specific Brand Benchmarking System Based On Social Media Strength Of A Brand

ABSTRACT

A brand monitoring platform (BMP) for brand benchmarking based on a brand&#39;s social media strength is provided. The BMP acquires input information on the brand and identifies industries related to the brand and competing brands. The BMP acquires social media information related to the brand and the competing brands from multiple social media sources via a network, dynamically generates categories in one or more hierarchical levels in each of the industries based on an independent analysis of the social media information, and sorts the social media information into the categories using a sorting interface. The BMP generates an aggregate score using an audience score determined by measuring an aggregate reach of the brand and the competing brands based on weighted audience score metric parameters, and an engagement score determined by measuring interaction between the brand and the competing brands and their followers based on weighted engagement score metric parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 13/587,928, titled “INDUSTRY SPECIFIC BRANDBENCHMARKING SYSTEM BASED ON SOCIAL MEDIA STRENGTH OF A BRAND”, filedAug. 17, 2012 in the United States Patent and Trademark Office. Thespecification of the above reference patent application is incorporatedherein by reference in its entirety.

This application also claims the benefit of non-provisional patentapplication number 2232/CHE/2012 titled “INDUSTRY SPECIFIC BRANDBENCHMARKING SYSTEM BASED ON SOCIAL MEDIA STRENGTH OF A BRAND”, filed onJun. 4, 2012 in the Indian Patent Office. The specification of the abovereferenced non-provisional patent application is incorporated herein byreference in its entirety.

BACKGROUND

One of the factors that determines the success of a corporateorganization is visibility of its brand in various media spaces. Withthe rapidly rising influence of social media networks, for example, suchas Facebook® of Facebook, Inc., Twitter® of Twitter Inc., etc., on brandmarketing, there is a need for comparing brands against theircompetitors to know how and where they stand among their competitors orpeers in a social media space. Conventional benchmarking systemstypically perform a brand comparison only based on the reach of thebrand within the social media space. These conventional benchmarkingsystems often perform brand comparison for brands in disparate fields,unrelated industries, unrelated geographical areas, etc. A genericbenchmarking system that compares brands in unrelated industries isoften not useful since demographics of consumers, market forces, etc.,that drive different industries are often different. Furthermore, brandsin different industries, or brands concentrated in a particulargeographical location often adopt different methods of socialinteraction. Therefore, there is a need for benchmarking brands againstother brands that operate in the same social space.

Furthermore, conventional benchmarking systems do not take into accountdifferences arising due to variations in geographical locations of thebrand. Therefore, when a conventional benchmarking system generatesbenchmark scores for an entire industry, systemic high scores receivedby a brand in a particular geographical location often overpowersystemic low scores received by the brand in another geographicallocation, resulting in a skewed combined industry score. For example, abrand for a cellular network provider may have a large market in aparticular geographical location, and consequently a larger consumerbase in that particular geographical location. Therefore, the brand mayhave a larger following on a social media source commonly used byconsumers located in that particular geographical location. However, thebrand may have to contend with multiple competing brands in ageographical location where the brand is yet to establish a sizeablemarket. Furthermore, consumers in the other geographical location maynot be inclined to use a social media source for brand interaction.Therefore, a benchmark score generated for the brand in a particulargeographical location may not be comparable with a benchmark scoregenerated for the brand in a different geographical location.

Conventional benchmarking systems often generate a universal score thatdoes not consider factors affected by a geographical location of thebrand. Therefore, there is a need for a computer implemented method andsystem that benchmarks brands and generates benchmark scores specific toan industry related to the brand and its competitors and/or ageographical location at which the brands operate. Furthermore, sincethere is a wide variation among brands across different industries in atargeted market, demographics, brand messages, actual products,marketing strategies adopted by the brands, etc., there is a need for acomputer implemented method and system that provides a focused benchmarkscore for a brand that is valid to a product and/or a service categoryfor which the product and/or the service represented by the brand wasdeveloped, without comparing benchmark scores of two differentindustries.

Hence, there is a long felt but unresolved need for a computerimplemented method and system that benchmarks a brand based on socialmedia strength of the brand relative to other competing brands operatingin the same industry and/or the same geographical location as that ofthe brand.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in asimplified form that are further disclosed in the detailed descriptionof the invention. This summary is not intended to identify key oressential inventive concepts of the claimed subject matter, nor is itintended for determining the scope of the claimed subject matter.

The computer implemented method and system disclosed herein addressesthe above stated need for benchmarking a brand based on social mediastrength of the brand relative to other competing brands operating inthe same industry and/or the same geographical location as that of thebrand. The computer implemented method and system disclosed hereinprovides a brand monitoring platform comprising at least one processorconfigured to monitor the brand in a virtual social media environment.As used herein, the term “social media strength” refers to a measure ofstrength of consumer reach and consumer interaction supported by a brandin a virtual social media environment. Also, as used herein, the term“virtual social media environment” refers to an environment comprisingsocial media networks and forums that enable interaction between brandowners and/or marketers and brand followers, consumers, etc.

The brand monitoring platform acquires input information on the brand.The brand monitoring platform identifies industries related to the brandand competing brands in the identified industries using the acquiredinput information on the brand. The brand monitoring platform acquiressocial media information related to the brand and the competing brandsin the identified industries from multiple social media sources in thevirtual social media environment via a network. As used herein, the term“social media source” refers to an online social platform, for example,an internet forum, a blog, a social network, etc., that enablesconsumers, brand followers, etc., to network and access information on abrand, discuss brands, establish a brand community, communicate withbrand owners and/or marketers, post responses to events or informationon products and/or services related to the brands, etc.

The brand monitoring platform dynamically generates categories in one ormore hierarchical levels in each of the identified industries based onan independent analysis of the acquired social media information relatedto the brand and the competing brands from each of the social mediasources. The dynamically generated categories comprise, for example, alocation of each of the identified industries related to the brand andeach of the competing brands, a location of each of multiple authors ofthe social media information, types of social media sources utilized bythe brand and each of the competing brands, marketing elements such asspecial discount offers, incentives, etc. In an embodiment, the brandmonitoring platform determines clusters of similar content portions fromthe acquired social media information and identifies one or more commoncategories applicable to the brand and each of the competing brands ineach of the identified industries from the determined clusters ofsimilar content portions for dynamic generation of the categories.

The brand monitoring platform sorts the acquired social mediainformation related to the brand and the competing brands in each of theidentified industries into one or more of the dynamically generatedcategories in one or more hierarchical levels using a sorting interfaceprovided by the brand monitoring platform. The brand monitoring platformacquires inputs configured, for example, as tags, for sorting theacquired social media information related to the brand and the competingbrands in each of the identified industries into one or more of thedynamically generated categories in one or more of the hierarchicallevels from a user via the sorting interface.

The brand monitoring platform determines an audience score for the brandand each of the competing brands by measuring an aggregate reach of thebrand and each of the competing brands in the virtual social mediaenvironment based on one or more of multiple weighted audience scoremetric parameters using the sorted social media information. Theweighted audience score metric parameters comprise, for example, anumber of followers of the brand and each of the competing brands ateach of the social media sources, a rate of growth of the number offollowers of the brand and each of the competing brands, a number ofrecommendations for the brand and each of the competing brands at eachof the social media sources from each of the followers, a number ofreferences made to the brand and each of the competing brands at each ofthe social media sources by the followers, aggregate responses toproducts, services, and/or events associated with the brand and each ofthe competing brands, etc.

In an embodiment, the brand monitoring platform normalizes measurescorresponding to each of the audience score metric parameters. The brandmonitoring platform assigns individual weights to the audience scoremetric parameters. The brand monitoring platform then determines aweighted average of the normalized measures corresponding to each of theaudience score metric parameters using the assigned individual weightsto determine the audience score for the brand and each of the competingbrands. In an embodiment, the brand monitoring platform normalizesmeasures corresponding to each of the weighted audience score metricparameters for reducing statistical differences between extreme measurescorresponding to each of the weighted audience score parameters, and forreducing outlier data.

The brand monitoring platform determines an engagement score for thebrand and each of the competing brands by measuring interaction betweenthe brand and each of the competing brands and their correspondingfollowers based on one or more of multiple weighted engagement scoremetric parameters using the sorted social media information. Theweighted engagement score metric parameters comprise, for example,nature of responses to one or more brand actions of the brand and eachof the competing brands from each of the followers of the brand and eachof the competing brands, a number of brand notification messages,sentiments of the followers towards the brand and each of the competingbrands, a number of fan posts extracted from the acquired social mediainformation, and relevance of the fan posts to the brand and each of thecompeting brands. A post is an electronic entry, for example, in theform of a text message input by a fan, a follower, a brandadministrator, etc., at a social media source using a computing device.In an embodiment, the brand monitoring platform determines theengagement score for the brand and each of the competing brands bynormalizing measures corresponding to each of the engagement scoremetric parameters, assigning individual weights to the engagement scoremetric parameters, and determining a weighted average of the normalizedmeasures corresponding to each of the engagement score metric parametersusing the assigned individual weights.

In an embodiment, the brand monitoring platform configures one or moreweighted audience score metric parameters and one or more engagementscore metric parameters for determination of the audience score and theengagement score respectively, based on predetermined criteria.Furthermore, in an embodiment, the determination of the audience scoreand the engagement score for the brand and each of the competing brandsby the brand monitoring platform comprises normalizing measurescorresponding to one or more of the audience score metric parameters andone or more of the engagement score metric parameters respectively,based on a location of each of the identified industries related to thebrand and each of the competing brands, for reducing statisticaldifferences in the measures triggered by a difference of the location ofeach of the identified industries related to the brand and each of thecompeting brands. Furthermore, in an embodiment, the brand monitoringplatform normalizes measures corresponding to each of the weightedengagement score metric parameters for reducing statistical differencesbetween extreme measures corresponding to each of the weightedengagement score parameters, and for reducing outlier data.

The brand monitoring platform generates an aggregate score for the brandand each of the competing brands using the determined audience score andthe determined engagement score. In an embodiment, the brand monitoringplatform generates the aggregate score for the brand and each of thecompeting brands by determining a weighted average of the determinedaudience score and the determined engagement score. The brand monitoringplatform assigns a rank to the brand and each of the competing brandsbased on the aggregate score for benchmarking the brand based on thesocial media strength of the brand in comparison with the competingbrands in the virtual social media environment. The generated aggregatescore of the brand and each of the competing brands benchmarks the brandbased on the social media strength of the brand in comparison with thecompeting brands in the virtual social media environment.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofthe invention, is better understood when read in conjunction with theappended drawings. For the purpose of illustrating the invention,exemplary constructions of the invention are shown in the drawings.However, the invention is not limited to the specific methods andcomponents disclosed herein.

FIGS. 1A-1B illustrate a computer implemented method for benchmarking abrand based on social media strength of the brand.

FIG. 2 exemplarily illustrates a flowchart comprising the stepsperformed by a brand monitoring platform for generating an aggregatescore for benchmarking a brand based on the social media strength of thebrand.

FIG. 3 exemplarily illustrates a flowchart comprising the stepsperformed by the brand monitoring platform for dynamically generatingcategories in each of multiple industries related to a brand andcompeting brands.

FIG. 4 exemplarily illustrates a schematic diagram indicating metricparameters used for determining an audience score and an engagementscore.

FIG. 5 exemplarily illustrates a flowchart comprising the stepsperformed by the brand monitoring platform for normalizing measurescorresponding to audience score metric parameters or engagement scoremetric parameters.

FIG. 6 exemplarily illustrates a schematic diagram for determining anaudience score for a brand.

FIG. 7 exemplarily illustrates a schematic diagram for determining anengagement score for a brand.

FIG. 8 exemplarily illustrates a flowchart comprising the steps fordetermining an aggregate score for a brand.

FIGS. 9A-9D exemplarily illustrate screenshots of a sorting interfaceprovided by the brand monitoring platform for sorting social mediainformation into one or more dynamically generated categories.

FIG. 10A exemplarily illustrates a table displaying results of acomputation of an audience score for each of multiple brands in anindustry using social media information acquired from a particularsocial media source.

FIG. 10B exemplarily illustrates a table displaying results of acomputation of an engagement score for each of multiple brands in anindustry using social media information acquired from a particularsocial media source.

FIG. 10C exemplarily illustrates a table displaying results of acomputation of an aggregate score for each of multiple brands in anindustry.

FIGS. 11A-11B exemplarily illustrate screenshots of a graphical userinterface provided by the brand monitoring platform, displayingaggregate scores generated for multiple brands in particular industries.

FIG. 12 exemplarily illustrates a screenshot of a graphical userinterface provided by the brand monitoring platform displaying agraphical representation of a comparative analysis of an audience scoreagainst an engagement score for each of multiple brands in a particularindustry.

FIG. 13 exemplarily illustrates a flowchart comprising the steps forbenchmarking a brand based on the social media strength of the brand ina particular industry.

FIG. 14 illustrates a computer implemented system for benchmarking abrand based on the social media strength of the brand.

FIG. 15 exemplarily illustrates the architecture of a computer systememployed by the brand monitoring platform for benchmarking a brand basedon the social media strength of the brand.

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 1A-1B illustrate a computer implemented method for benchmarking abrand based on social media strength of the brand. As used herein, theterm “social media strength” refers to a measure of strength of consumerreach and consumer interaction supported by a brand in a virtual socialmedia environment. Also, as used herein, the term “virtual social mediaenvironment” refers to an environment comprising social media networksand forums that enable interaction between brand owners and/or marketersand brand followers, consumers, etc. The computer implemented methoddisclosed herein provides 101 a brand monitoring platform comprising atleast one processor configured to monitor the brand in a virtual socialmedia environment. The brand monitoring platform is, for example, hostedon an online server. In an embodiment, the brand monitoring platformprovides a web application accessible over a network such as theinternet or an intranet that performs scoring for benchmarking a brandbased on the social media strength of the brand and transmits theresults to a database of an online server via the network. The databasestores records of scores computed for each of the brands, therebyenabling tracking of growth in market strength of a brand over apredetermined duration of time. The brand monitoring platform monitorsvisibility of the brand via social media sources and performs ananalysis of each response received from online users, brand followers,etc., to events related to the brand, information released on productsand/or services offered by the brand, etc., for generating scores thatenable comparison between different brands in an industry, therebyproviding a benchmarking system for brands. The brand monitoringplatform provides a scoring system for brands that reflects their socialmedia strength relative to other brands in the same social media space.

The brand monitoring platform acquires 102 input information on thebrand. The input information on the brand comprises, for example, nameof the brand, information on products and/or services associated withthe brand, demographics of consumers targeted by the brand, geographicalmarketing data of the brand, market share of the brand, etc. In anembodiment, the brand monitoring platform crawls the web to extractinformation on the brand based on a preliminary set of inputs receivedfrom a brand marketing entity associated with the brand. The brandmonitoring platform extracts the input information, for example, fromonline advertisements, images, videos, consumer forums, press releases,news, events, white papers, etc., of the brand. The brand monitoringplatform creates a brand profile based on the acquired input informationfor each of the brands and tracks and updates changes to the brandprofile periodically. In an example, the brand monitoring platformconfigures application programming interfaces (APIs) for each ofmultiple online resources comprising, for example, social media sourcesfor automatically retrieving information on a particular brand.

The brand monitoring platform identifies 103 industries related to thebrand and competing brands in the identified industries using theacquired input information on the brand. For example, the brandmonitoring platform establishes communication with online retail systemsvia a network, for example, the internet and extracts information onproducts and/or services with characteristics similar to the productsand/or services associated with the brand. The brand monitoring platformidentifies brands associated with the products and/or the serviceshaving similar characteristics as those of the input brand as “competingbrands”.

In another example, the brand monitoring platform accesses online publicdatabases via the network to obtain industry specific information for abrand and competing brands in an industry. The brand monitoring platformestablishes a connection with databases of multiple social media sourcesover the network. For example, the brand monitoring platform accesses aweb page of a brand hosted on Facebook® of Facebook, Inc. The web pageof the brand on Facebook® may list the industries to which the brandbelongs. Furthermore, brand information related to the brand isretrieved through an application programming interface (API) customizedfor accessing brand information from Facebook®. Furthermore, in order toobtain brand information on the competing brands for an industry, thebrand monitoring platform establishes a connection to public databasesvia the network to query and obtain the information related to each ofthe competing brands. For example, the brand monitoring platformconnects to Yahoo!® Finance of Yahoo, Inc., and retrieves brandinformation, finance information of the competing brands within anindustry via an API access. Furthermore, the brand monitoring platformuses, for example, the Facebook® API to obtain brand information of thecompeting brands in the industry from a Facebook® web page.

In an embodiment, the brand monitoring platform identifies industriesusing the brand and the competing brands. The brand monitoring platformidentifies multiple industries that are associated with a particularbrand. A company associated with a brand, for example, provides productsand/or services across multiple industries. Consider an example where aparticular brand is associated with industries such as healthcare,aviation, fuel and energy management, capital management, etc. The brandmonitoring platform identifies competing brands for that particularbrand in each of the identified industries and determines from the brandinformation that the market for the brand extends to multipleindustries. The brand monitoring platform benchmarks the brandseparately in each of the different identified industries.

The brand monitoring platform acquires 104 social media informationrelated to the brand and the competing brands in the identifiedindustries from multiple social media sources in the virtual socialmedia environment via a network, for example, the internet, an intranet,a local area network, a wide area network, a communication networkimplementing Wi-Fi® of the Wireless Ethernet Compatibility Alliance,Inc., a cellular network, a mobile communication network such as aglobal system for mobile communications (GSM) network, a general packetradio service (GPRS) network, etc. As used herein, the term “socialmedia source” refers to an online social platform, for example, aninternet forum, a blog, a social network, etc., that enables consumers,brand followers, etc., to network and access information on a brand,discuss brands, establish a brand community, communicate with brandowners and/or marketers, post responses to events or information onproducts and/or services related to the brands, etc. A brand communityis, for example, a group of followers or consumers interested in theproducts and/or services associated with a brand. A social media sourceis, for example, Facebook® of Facebook, Inc., Twitter® of Twitter, Inc.,Google+™ of Google, Inc., YouTube® of Google, Inc., LinkedIn® ofLinkedIn Corporation, etc. The social media information comprises, forexample, statistical information on number of followers of a brand at aparticular social media source, number of posts posted by brandadministrators, brand followers, brand marketers, etc., for example, anumber of tweets received for a brand on Twitter®, or a number of shareson Facebook®, the content of the posts, etc. A post is an electronicentry, for example, in the form of a text message input by a fan, afollower, a brand administrator, etc., at a social media source using acomputing device. A “tweet” refers to a post made on a micro-bloggingwebsite of Twitter®.

The brand monitoring platform dynamically generates 105 categories inone or more hierarchical levels in each of the identified industriesbased on an independent analysis of the acquired social mediainformation related to the brand and the competing brands from each ofthe social media sources. The hierarchical levels comprise, for example,a set of sub-categories for each of the dynamically generated categoriesin each of the identified industries. The dynamically generatedcategories comprise, for example, a location of each of the identifiedindustries related to the brand and each of the competing brands, alocation of each of multiple authors of the social media information,types of social media sources utilized by the brand and each of thecompeting brands, marketing elements, etc. The categorization of thesocial media information based on the location of each of the identifiedindustries enables a relative analysis of the social media strength ofthe brands in line with the preferences of consumers in a particulargeographical location. The location of each of the authors of the socialmedia information, for example, followers who regularly post messages,their reviews of products and/or services associated with the brand,etc., determine a scale of interest in the brand and products and/orservices associated with the brand for consumers located in theparticular location. The types of social media sources utilized by thebrand and each of the competing brands comprise, for example, differentsocial networking applications such as Facebook® of Facebook, Inc.,Twitter® of Twitter, Inc., Google+™ of Google, Inc., YouTube® of Google,Inc., LinkedIn® of LinkedIn Corporation, etc., utilized by the brand andeach of the competing brands. The marketing elements comprise, forexample, special discount offers, incentives, etc., redeemable forpurchasing the products and/or services associated with the brand. Thebrand monitoring platform classifies information on the brands and thecompeting brands for analysis and generation of an aggregate score. Theaggregate score represents a relative position of the brand, among itspeers and competing brands, in its use of social media.

Consider an example where a brand owner registers a brand in an airlinesindustry with the brand monitoring platform. The brand monitoringplatform analyzes the acquired social media information and determines afirst hierarchical level of categories for categorizing the social mediainformation related to brands in the airlines industry, for example, as“brand related”, “current events”, “industry related”, and“miscellaneous”. The brand monitoring platform further divides each ofthe categories into a second hierarchical level of categories. Thesecond hierarchical level of categories is, for example, based on aparticular type of industry. For example, the brand monitoring platformdivides the “brand related” category of the first hierarchical levelinto a second hierarchical level of sub-categories, for example, airfaredeals, announcements and updates, brand news, corporate socialresponsibility, marketing elements such as contests or sweepstakes,events, festive offers, frequent flyer programs, frequently askedquestions pertaining to the brand, travel destination information, etc.The brand monitoring platform divides the “current events” category ofthe first hierarchical level into a second hierarchical level ofsub-categories, for example, festival and/or greetings posted byfollowers or the brand marketers, entertainment, events on social media,sports events such as cricket tournaments sponsored by the airlinesindustry, questions, miscellaneous, etc. The brand monitoring platformdivides the “industry related” category of the first hierarchical levelinto a second hierarchical level of sub-categories, for example, eventsin the airline industry, facts about the airline industry, questions tofollowers, news on sectors such as fluctuations recorded in the sharemarket, travel advice, travel destination information, etc. The“miscellaneous” category of the first hierarchical level comprises, forexample, information about the airlines industry that is not related toa specific brand.

In an embodiment, the brand monitoring platform performs an independentanalysis of the acquired social media information related to the brandand the competing brands from each of the social media sources fordynamically generating categories by determining clusters of similarcontent portions from the acquired social media information andidentifying one or more common categories applicable to the brand andeach of the competing brands in each of the identified industries fromthe determined clusters of similar content portions. The brandmonitoring platform analyzes a large number of posts retrieved frommultiple social media sources. In an embodiment, the brand monitoringplatform performs a keyword analysis and a clustering of the posts toextract common patterns, keywords, etc. For example, the brandmonitoring platform applies a clustering computer program for comparingcontent portions extracted from the social media information acquiredfrom each of the social media sources and generates a similarity scorebased on a keyword comparison between the content portions. The brandmonitoring platform identifies common keywords from the clusteredcontent portions for generating categories. In an example, the brandmonitoring platform performs a natural language processing analysis oftextual content in the acquired social media information to extractpatterns of linguistic similarity between the content portions.

Furthermore, in another example, the brand monitoring platform acquiresa set of categories from an authorized user via a graphical userinterface (GUI) provided by the brand monitoring platform, sorts theacquired social media information into the acquired categories, and thenperforms further analysis of the sorted social media information toidentify one or more common sub-categories from the acquired socialmedia information applicable to the brand and each of the competingbrands in the identified industries. The brand monitoring platformprovides a sorting interface on the GUI that enables users to manuallytag the keywords and/or the clusters of keywords as separate categoriesfor categorizing the acquired social media information and forgenerating aggregate scores for each of the brands. The brand monitoringplatform supports and maintains the dynamically generated categoriesalong with categories acquired from one or more users. The brandmonitoring platform provides an integrated set of categories that areavailable to users for sorting social media information, for example,posts recorded at a particular social media source.

Consider an example where the brand monitoring platform acquires socialmedia information, for example, posts posted by users at a social mediasource, for example, Facebook® on a product and/or a service released bya new brand in the market. The brand monitoring platform extractskeywords from each of the posts and compares these keywords with thekeywords extracted from each of the other posts to obtain clusters ofsimilar content portions. For example, consider a brand that releases anew soft drink and employs a tagline comprising keywords such as “gamechanger”. The brand monitoring platform excludes all qualifier keywordssuch as “excellent”, “awesome”, etc., from the posts during anindependent analysis of each of the posts. Furthermore, the posts andmessages may recite and reference the tagline of the brand. The brandmonitoring platform collects all the posts that have used the keyword“game”. In this example, most of the posts may also have the keywords“drink”, “beverage”, “thirst”, “bottle”, etc. The brand monitoringplatform clusters all the posts with similar content portions, forexample, the posts that reference the keywords “drink”, “beverage”,“bottle”, etc., together. The brand monitoring platform determines ahigher similarity between the keywords “drink” and “beverage” than thekeywords “beverage” and “game”. The brand monitoring platform assigns asimilarity score based on the amount of similarity between the keywordsextracted from the posts. Therefore, the brand monitoring platformassigns a higher similarity score for associated keywords such as“drink” and “beverage” and clusters these keywords together. Therefore,even though there is a high incidence of the keyword “game” through theposts, the brand monitoring platform does not categorize the brand asone related to sports since the similarity between the keywords “game”and “drink” is minimal. Based on the similarity score, the brandmonitoring platform identifies a common category, “Food and beverage”for the brand.

The brand monitoring platform sorts 106 the acquired social mediainformation related to the brand and the competing brands in each of theidentified industries into one or more of the dynamically generatedcategories in one or more of the hierarchical levels using the sortinginterface provided by the brand monitoring platform. The sortinginterface is, for example, hosted on the graphical user interface (GUI)provided by the brand monitoring platform. The brand monitoring platformacquires inputs, for example, as tags, for sorting the acquired socialmedia information related to the brand and the competing brands in eachof the identified industries into one or more of the dynamicallygenerated categories in the hierarchical levels from a user via thesorting interface. The sorting interface displays the complete set ofcategories and sub-categories for each of the identified industries andprovides interface options for manually tagging the social mediainformation, for example, posts from a social media source to aparticular category or a sub-category. The brand monitoring platformtherefore allows a user to manually tag posts into the categoriesgenerated by the brand monitoring platform or the categories acquiredfrom one or more users and maintained by the brand monitoring platform,for example, based on the industry related to the brand. The provisionfor manual tagging allows sorting of the social media information basedon user preferences. The steps for sorting the acquired social mediainformation are disclosed in the detailed description of FIGS. 9A-9D.

The brand monitoring platform determines 107 an audience score for thebrand and each of the competing brands by measuring an aggregate reachof the brand and each of the competing brands in the virtual socialmedia environment based on one or more of multiple weighted audiencescore metric parameters using the sorted social media information. Theterm “aggregate reach” refers to an extent of visibility that a brandhas garnered at a particular social media source. The weighted audiencescore metric parameters comprise, for example, a number of followers ofthe brand and each of the competing brands at each of the social mediasources, a rate of growth of the number of followers of the brand andeach of the competing brands, a number of recommendations for the brandand each of the competing brands at each of the social media sourcesfrom each of the followers, a number of references made to the brand andeach of the competing brands at each of the social media sources by thefollowers, aggregate responses to products, services, and/or eventsassociated with the brand and each of the competing brands, etc. Forexample, the brand monitoring platform defines audience score metricparameters as a number of fans or followers for the brand at socialmedia sources such as Facebook®, Twitter®, Google+™, etc., the rate ofgrowth of the fans, that is, how quickly the number of fans are growingin a brand community at each of the social media sources,recommendations from the followers, number of online consumers whomention the brand, aggregate responses such as “retweets”, “shares”,“likes”, etc., from the fans or followers, the commercial or market sizeof the brand, etc. A “share” refers to a sharing action performed by auser at a social media source using a share option provided by thesocial media source for sharing content between users. A “like” refersto a positive input provided by a user at a social media source using alike option provided by the social media source for expressing thathe/she likes, enjoys, or supports particular content.

In an embodiment, the brand monitoring platform normalizes measurescorresponding to each of the audience score metric parameters.Normalization is a technique that allows data on different scales to becompared by bringing them to a common scale. As used herein, the term“measures” refers to values of the metric parameters. The brandmonitoring platform assigns individual weights to the audience scoremetric parameters as disclosed in the detailed description of FIG. 6.The brand monitoring platform determines a weighted average of thenormalized measures corresponding to each of the audience score metricparameters using the assigned individual weights to determine theaudience score.

The brand monitoring platform determines 108 an engagement score for thebrand and each of the competing brands by measuring interaction betweenthe brand and each of the competing brands and their correspondingfollowers based on one or more of multiple weighted engagement scoremetric parameters using the sorted social media information. Theweighted engagement score metric parameters comprise, for example,nature of responses to one or more brand actions of the brand and eachof the competing brands from each of the followers of the brand and eachof the competing brands, a number of brand notification messages,sentiments of the followers towards the brand and each of the competingbrands, a number of fan posts extracted from the acquired social mediainformation, relevance of the fan posts to the brand and each of thecompeting brands. As used herein, the term “brand action” refers to anevent or an action carried out by a particular brand that affectsconsumers of the brand. A brand action is, for example, a release of anew product associated with the brand, announcement of discount offers,incentives, etc. The brand monitoring platform sets engagement scoremetric parameters, for example, how fans respond to the brand actions,for example, through likes, comments, shares, +1s, retweets, replies, upvotes, down votes, etc., how much effort the brand puts into a web pageat a social media source using number of posts, tweets, videos, photos,links, polls, brand messages, advertisements, etc., posted by brandmarketers, an extent of involvement of the fans in the brand communitybased on the number of fan posts, relevance of the fan posts to thebrand, how the fans identify with the brand, the sentiment of the fanstowards the brand, that is, whether the fans post messages reflecting apositive sentiment, a negative sentiment, or a neutral sentiment, etc.The brand monitoring platform performs sentiment analysis of, forexample, the fan responses, comments, replies, video responses,mentions, etc., to determine the engagement score.

In an embodiment, the brand monitoring platform determines an engagementscore for the brand and each of the competing brands by normalizingmeasures corresponding to each of the engagement score metricparameters, assigning individual weights to the engagement score metricparameters, and determining a weighted average of the normalizedmeasures corresponding to each of the engagement score metric parametersusing the assigned individual weights.

In an embodiment, the brand monitoring platform configures one or moreweighted audience score metric parameters and one or more weightedengagement score metric parameters for determination of the audiencescore and the engagement score respectively, based on predeterminedcriteria. For example, on completing sorting and categorization of theacquired social media information, the brand monitoring platform hasaccess to information on the type of content posted by the brands. Thebrand monitoring platform uses a percentage of posts related to thebrand, or a percentage of posts related to the industry as metricparameters to be used for computing an aggregate score. Thepredetermined criteria for configuring the metric parameters are setdifferently for different social media sources. For example, for aparticular social media source such as Facebook®, the brand monitoringplatform assigns a higher weight for a percentage of posts related to abrand than to a percentage of posts related to an industry where thebrands are operative.

In an embodiment, the brand monitoring platform retrieves the socialmedia information comprising measures for each of the audience scoremetric parameters and the engagement score metric parameters viaapplication programming interfaces (APIs) generated for each of thesocial media sources. For example, the brand monitoring platform, incollaboration with each of the social media sources, generates separateapplication programming interfaces for Facebook®, Twitter®, LinkedIn®,etc. The brand monitoring platform configures the APIs for each of thesocial media sources to automatically retrieve the social mediainformation related to the brand and the competing brands in theidentified industries from each of the social media sources in thevirtual social media environment.

Furthermore, in an embodiment, the determination of the audience scoreand the engagement score for the brand and each of the competing brandsby the brand monitoring platform comprises normalizing measurescorresponding to the audience score metric parameters and the engagementscore metric parameters respectively, based on a location of each of theidentified industries related to the brand and each of the competingbrands, for reducing statistical differences in the measures triggeredby a difference of the location of each of the identified industriesrelated to the brand and each of the competing brands. Consider anexample where a particular brand has established a relatively largermarket in a particular geographical location when compared to anothergeographical location where the brand is yet to establish a sizeablemarket. Furthermore, the nature of a product associated with the brandmay affect the suitability of the product to a particular geographicallocation. For example, a skin tanning cream may have a greater market ina cold country than in a country located in an equatorial region.Therefore, the number of posts related to the skin tanning creamretrieved from a particular social media source, from consumers in thecold country is statistically higher than the number of posts fromconsumers in the country located in the equatorial region. However, asun protection cream offered by the same brand may trigger a greaterresponse to the product on that particular social media source fromconsumers in the equatorial region. Therefore, this may result in askewed analysis of a particular brand and all the competing brands. Inorder to balance the statistical differences induced by changes ingeography, demographics, etc., the brand monitoring platform normalizesthe measures corresponding to one or more of the audience score metricparameters or the engagement score metric parameters that are affectedby changes in the geography. This ensures that when an aggregate scoreis computed for all the brands in an industry, systemic high aggregatescores for brands in a particular geographical location do not overpowersystemic low scores of the brands in a different geographical location.In an example, the normalization of the measures corresponding to theaudience score metric parameters and the engagement score metricparameters comprises statistically processing their respective measuresto ensure a match between a median value and a variance value for eachof the locations of the identified industries related to the brand andthe competing brands.

In an embodiment, the brand monitoring platform normalizes measurescorresponding to each of the weighted audience score metric parametersfor reducing statistical differences between extreme measurescorresponding to each of the weighted audience score parameters, and forreducing outlier data. Furthermore, in an embodiment, the brandmonitoring platform normalizes measures corresponding to each of theweighted engagement score metric parameters for reducing statisticaldifferences between extreme measures corresponding to each of theweighted engagement score parameters, and for reducing outlier data. Theoutlier data comprise one or more values of the measures correspondingto the audience score metric parameters or the engagement score metricparameters that deviate markedly from the other values of the measures.

The statistical differences between the measures corresponding to eachof the metric parameters affect a variance and consequently a standarddeviation of a statistical distribution of the measures corresponding toeach of the metric parameters. For example, a comparative analysisbetween a brand and a competing brand with respect to an audience scoremetric parameter “growth of a number of fans” may be inaccurate due to alarge variance between extreme measures in the statistical distributiontriggered by outlier data in the statistical distribution of one of thebrands. The outlier data may be recorded as a result of an abruptincrease in the number of fans for a brand for a short period of time.Furthermore, larger statistical differences between the measures of ametric parameter affect a mean value of the statistical distribution forthat metric parameter. Therefore, a comparative analysis between a brandand a competing brand based on an average number of fans for each of thebrands is likely to be inaccurate since the mean value is affected bythe extreme measures of the statistical distribution. The brandmonitoring platform therefore normalizes the audience score and theengagement score by statistically processing the audience score and theengagement score respectively to have an identical mean value andvariance value.

The brand monitoring platform generates 109 an aggregate score for thebrand and each of the competing brands using the determined audiencescore and the determined engagement score. The brand monitoring platformgenerates a separate aggregate score for each brand for each socialmedia source used by the brand. In an embodiment, the brand monitoringplatform generates the aggregate score for the brand and each of thecompeting brands by determining a weighted average of the determinedaudience score and the determined engagement score. In an embodiment,the brand monitoring platform performs normalization, for example, byconverting the audience scores and the engagement scores to a commonmedian and a common standard deviation to allow the audience scores andthe engagement scores to be combined to generate the aggregate scores.The aggregate score is specific to the industry and/or geography inwhich the brand operates. In an example, the brand monitoring platformgenerates an aggregate score exclusively, that is, an aggregate scorethat is only valid in a category for which the aggregate score isdeveloped, and excludes comparison of aggregate scores between twoindustries. This enables benchmarking for brands against theircompetitors or peers in a similar industry. Therefore, considering thewide variation among brands in a targeted market, demographics, brandmessages, actual products and/or services, marketing strategies etc., anarrowly focused aggregate score reflects a more balanced analysis ofbrands in a particular industry.

The aggregate score is an independent score that can be used by brandowners to track the pulse of enterprise social activity withinindustries. The aggregate score can be used by brand owners of, forexample, new brands to ascertain their position among peers andcompeting brands in their use of social media. The aggregate score canbe used by brand owners of, for example, lagging brands to dissect andunderstand strategies used by top ranking brands to achieve their topscores. The aggregate score can be used by brand owners to validate thattheir social media strategy is working as intended by monitoring thechanges in their aggregate score relative to all socially active brands.

The brand monitoring platform assigns a rank to the brand and each ofthe competing brands based on the aggregate score for benchmarking thebrand based on the social media strength of the brand in comparison withthe competing brands in the virtual social media environment. Forexample, a brand assigned with a rank of one may be considered as aleading brand for a particular industry in a particular geographicallocation. The aggregate score and the brand rank of a brand and each ofthe competing brands benchmarks the brand based on the social mediastrength of the brand in comparison with the competing brands in thevirtual social media environment.

In an embodiment, the brand monitoring platform tracks changes to theacquired social media information, input information acquired on thebrands, etc., and automatically updates the audience score, theengagement score, the aggregate score, and the brand rank of each of thebrands in the industry. The brand monitoring platform transmits anotification to each of the brand communities, brand marketers, etc.,who have registered with the brand monitoring platform, for example, viaan electronic mail (email) notification, a pop-up message on a displaywindow, etc., on the updated changes to the aggregate score and thebrand rank.

FIG. 2 exemplarily illustrates a flowchart comprising the stepsperformed by the brand monitoring platform for generating an aggregatescore for benchmarking a brand based on the social media strength of thebrand. In order to generate 201 an aggregate score, the brand monitoringplatform acquires inputs, for example, whether the brand needs to bebenchmarked by the industry or the geographical location 213 of thebrand, the measures corresponding to the audience score metricparameters, for example, fan size 205, fan growth 206, number of“mentions” 207 of a product and/or services offered by the brand, and abrand size 208, and the measures corresponding to the engagement scoremetric parameters, for example, fan response 209 of each of thefollowers of the brand, brand effort 210 quantified by a number ofupdates to the brand information, incentives, notifications, etc.,posted by the brand marketers, an extent of fan involvement 211, fansentiment 212, that is whether the brand has received posts reflecting apositive sentiment, a negative sentiment, or a neutral sentiment, fromthe fans, etc. The brand monitoring platform performs statisticalanalysis and processing 204 to normalize the different measurescorresponding to each of the audience score metric parameters and theengagement score metric parameters such that statistical differencestriggered by changes in industry and/or geography are reduced. The brandmonitoring platform determines 202 the audience score based on theaudience score metric parameters and determines 203 the engagement scorebased on the engagement score metric parameters. The brand monitoringplatform generates 201 an aggregate score using the audience score andthe engagement score. For example, the brand monitoring platformcomputes a weighted average of the audience score and the engagementscore to generate the aggregate score.

FIG. 3 exemplarily illustrates a flowchart comprising the stepsperformed by the brand monitoring platform for dynamically generatingcategories in each of multiple industries related to a brand andcompeting brands. Consider an example where a brand, for example, acosmetic brand needs to be benchmarked based on its social mediastrength. The brand may be a part of multiple industries, for example, achemical industry, a cosmetics industry, etc. The brand monitoringplatform identifies 301 a particular industry for the brand. The brandmonitoring platform extracts 306, 307, and 308 social media informationfrom each of multiple social media sources, for example, a social mediasource 1 such as Facebook®, a social media source 2 such as Twitter®,and a social media source 3 such as YouTube® respectively. The brandmonitoring platform analyzes 302 general information on the particularindustry, including brand information and other social media informationrelated to the brand and the competing brands, extracted via applicationprogramming interfaces (APIs) associated with the social media sources1, 2, and 3 respectively.

The brand monitoring platform selects 303 important competing brands inthe identified industry and populates 303 social media informationattributes extracted from the social media information, for example, ina relational database management system. The brand monitoring platformemploys a clustering algorithm to collect the social media informationrelevant to all brands in a particular industry. The brand monitoringplatform analyzes 304 the social media information, for example, postspublished on a social media source for each of the brands. The brandmonitoring platform dynamically generates 305 industry categories forsorting the social media information using keywords extracted from thesocial media information. The brand monitoring platform provides asorting interface for manually tagging the social media information intothe dynamically generated categories, for example, based on an industryof each brand.

FIG. 4 exemplarily illustrates a schematic diagram indicating metricparameters used for determining an audience score and an engagementscore. The brand monitoring platform extracts social media informationvia an application programming interface (API) maintained for aparticular social media source. For example, the brand monitoringplatform acquires social media information from the Facebook® API foreach of the brands and extracts measures corresponding to the audiencescore metric parameters and the engagement score metric parametersstored in a metric parameter set 401. The measures are broadlyclassified, for example, into two types of measures. One of the measuresis related to quantitative aspects of the brand, that is, the size ofthe brand and the other measure relates to qualitative aspects of thebrand. The quantitative measures comprise, for example, number of fans402, growth of fans 403, that is, increase or decrease in the number offans over a period of time, number of administrator posts 404,percentage of fan posts 405, the number of consumers discussing aproduct or a service associated with the brand, represented by themetric parameter “number of people talking about this” 406, growth of“people talking about this” 407, number of brand related posts 408, thepercentage of brand related posts 409, etc. The qualitative measurescomprise, for example, an engagement number 410, a change in theengagement number 411, a net positive sentiment 412 of the posts derivedfrom the number and percentage of posts with positive sentiments, thepercentage of change in sentiment 413 over a period of time, etc. Thebrand monitoring platform computes the engagement number of a post basedon how the post engages with users using metric parameters, for example,a number of “shares”, a number of “comments”, a “number of likes”, etc.The brand monitoring platform then combines the engagement numbers forall the posts of a brand to obtain the engagement score for a brand. Thebrand monitoring platform analyzes the data collected over apredetermined duration of time, for example, over a month, forcalculating the audience score and the engagement score. The brandmonitoring platform computes the engagement score and the growth of theengagement score for a brand, for example, based on the number of likes,comments, shares for posts, the number and percentage of posts authoredby a brand or followers of the brand, the number and percentage of postsin each dynamically or manually generated category for a brand for eachindustry.

FIG. 5 exemplarily illustrates a flowchart comprising the stepsperformed by the brand monitoring platform for normalizing measurescorresponding to audience score metric parameters or engagement scoremetric parameters. The brand monitoring platform acquires 501 requiredinput information and social media information from each of theapplication programming interfaces (APIs) associated with each of thesocial media sources. The brand monitoring platform computes 502 therelevant measures for each of the audience score metric parameters andthe engagement score metric parameters, for example, by counting a totalnumber of posts for a particular brand on a particular social mediasource. The brand monitoring platform normalizes the measurescorresponding to each of the metric parameters to remove statisticaldifferences. The brand monitoring platform then processes the normalizedmeasures and computes 503 intermediate values for each of the measures.The intermediate values are values that allow easier mathematicalmanipulation for computation of the audience score or the engagementscore, and are therefore better suited for computation of the audiencescore or the engagement score. For example, for analyzing a sentiment ofeach of the posts posted by consumers, followers, etc., on a socialmedia source, the brand monitoring platform computes the intermediatevalues by first obtaining the net sentiment. The net sentiment iscomputed as: a positive sentiment percentage minus a negative sentimentpercentage. A shift operation is performed on the net sentiment valuessuch that a negative value of the net sentiment is converted to apositive value. For example, consider an audience score metric parameter“number of fans”. Since the measures of the audience score metricparameter could be very large or very small, the brand monitoringplatform computes an intermediate value equal to a square root of theaudience score metric parameter “number of fans”.

The brand monitoring platform normalizes the distribution of measuresfor one or more metric parameters that are affected by changes in thegeography by normalizing 504 median values of the measures according togeography. That is, the brand monitoring platform normalizes themeasures for each metric parameter by geography, in order to reducelarge statistical differences in the measures arising due to differencesin geographical locations of the brands. In another example fornormalization, the brand monitoring platform normalizes the measures bymaking a median and a variance similar for each geographical location.The brand monitoring platform also normalizes 505 median values of theother measures that are not geographically normalized, for the wholeindustry. For example, some quantitative measures may not be affected bygeography. Therefore, the brand monitoring platform does notgeographically normalize the measures that are not affected by geographyand instead normalizes these measures for the whole industry. The brandmonitoring platform performs normalization of the other measures bynormalizing variances in the measures. Furthermore, the normalization ofthe measures reduces the effect of outlier data.

The brand monitoring platform normalizes 506 extreme values in thedistribution of the measures by tapering the extreme values, asdisclosed in the detailed description of FIG. 13, to further reduce theeffect of outlier data. The brand monitoring platform normalizes extremevalues since the extreme values may be representative of outlier data.For example, all data corresponding to the measures of the audiencescore metric parameters and the engagement score metric parameterscontain some outlier data that is not representative of a sample of eachof the measures. The brand monitoring platform tamps down the extremevalues so that they do not affect the samples of the measures of therespective audience score metric parameters or the respective engagementscore metric parameters by a considerable extent. After normalization ofthe extreme values among the samples, the different measures cannot becompared to each other directly because each of the measures may havedifferent variances. Since comparing such measures directly may lead toerrors, the brand monitoring platform normalizes the variances such thatall measures have the same median and the same variance in order toensure that all the measures are comparable with each other.Furthermore, the brand monitoring platform then normalizes 507 astandard deviation of the measures for one or more audience score metricparameters and/or engagement score metric parameters.

FIG. 6 exemplarily illustrates a schematic diagram for determining anaudience score 607 for a brand. Consider a social media source 1, forexample, Facebook®. The brand monitoring platform configures an audiencescore metric parameter set 601 comprising audience score metricparameters associated with the social media source Facebook® forcomputing the audience score 607 for the brand. The brand monitoringplatform assigns different weights, for example, weight 1, weight 2,weight 3, weight 4, and weight 5 to the audience score metric parametersherein referred to as “quantitative metrics”. The weights assigned foreach of the quantitative metrics, for example, depend on the industryidentified for the brand. The quantitative metrics comprise, forexample, a number of fans 602, growth of fans 603, a number of people“talking about this” 604, that is, a number of people discussingproducts and/or services associated with the brand, at the social mediasource, a change in the number of people “talking about this” 604, andother audience score metric parameters 606.

The brand monitoring platform determines the audience score 607 using aweighted average of the normalized measures of the quantitative metrics.For example, the brand monitoring platform computes a mathematicalproduct of a measure of the quantitative metric “number of fans” 602with weight 1 and uses the computed mathematical product as one of theinputs for calculating the audience score 607. Similarly, the brandmonitoring platform computes a mathematical product of a measure of thequantitative metric “growth of fans” 603 with weight 2, a mathematicalproduct of a measure of the quantitative metric “number of peopletalking about this” 604 with weight 3, a mathematical product of ameasure of the quantitative metric “change in the number of peopletalking about this” 605 with weight 4, and a mathematical product of ameasure of the quantitative metric “other audience score metricparameters” 606 with weight 5. The brand monitoring platform thendetermines the audience score 607 using the computed mathematicalproducts as inputs. The weights determine the contribution of each ofthe quantitative metrics to the audience score 607.

FIG. 7 exemplarily illustrates a schematic diagram for determining anengagement score 711 for a brand. Consider a social media source 1, forexample, Facebook®. The brand monitoring platform configures anengagement score metric parameter set 701 comprising engagement scoremetric parameters associated with the social media source Facebook® forcomputing the engagement score 711 for the brand. The brand monitoringplatform assigns different weights, for example, weight 1, weight 2,weight 3, weight 4, weight 5, weight 6, weight 7, weight 8, and weight 9to each of the engagement score metric parameters herein referred to as“qualitative metrics”. The qualitative metrics comprise, for example, anengagement number 702, a change in the engagement number 703, a netpositive sentiment 704 analyzed from the posts extracted from theacquired social media information, a change in sentiment 705 of theposts, other engagement score metric parameters 706, a number ofadministrator posts 707, a percentage of fan posts 708, a number ofbrand related posts 709, and a percentage of brand related posts 710.

The brand monitoring platform determines the engagement score 711 usinga weighted average of the normalized measures of the qualitativemetrics. For example, the brand monitoring platform computes amathematical product of a measure of the qualitative metric “engagementnumber” 702 with weight 1 and uses the computed mathematical product asone of the inputs for calculating the engagement score 711. Similarly,the brand monitoring platform computes a mathematical product of ameasure of the qualitative metric “change in the engagement number” 703with weight 2, a mathematical product of a measure of the qualitativemetric “net positive sentiment” 704 with weight 3, a mathematicalproduct of a measure of the qualitative metric “change in sentiment” 705with weight 4, a mathematical product of a measure of the qualitativemetric “number of administrator posts” 707 with weight 6, a mathematicalproduct of a measure of the qualitative metric “percentage of fan posts”708 with weight 7, a mathematical product of a measure of thequalitative metric “number of brand related posts” 709 with weight 8, amathematical product of a measure of the qualitative metric “percentageof brand related posts” 710 with weight 9, and a mathematical product ofa measure of the qualitative metric “other engagement score metricparameters” 706 with weight 5. The brand monitoring platform thendetermines the engagement score 711 using the computed mathematicalproducts as inputs. The weights determine the contribution of each ofthe qualitative metrics to the engagement score 711.

FIG. 8 exemplarily illustrates a flowchart comprising the steps fordetermining an aggregate score for a brand. The brand monitoringplatform computes 801 an audience score as disclosed in the detaileddescription of FIG. 6, and normalizes 802 the audience score to ensurethat the normalized audience score has the same mean and variance. Thebrand monitoring platform then computes 803 the engagement score asdisclosed in the detailed description of FIG. 7, and normalizes 804 theengagement score to ensure that the normalized engagement score has thesame mean and variance. The brand monitoring platform computes aweighted average of the normalized audience score and the normalizedengagement score to generate 805 an aggregate score. The brandmonitoring platform normalizes the generated aggregate score to generate806 a normalized aggregate score as disclosed in the detaileddescription of FIG. 13. The brand monitoring platform assigns 807 abrand rank to the brand based on the normalized aggregate score andcompares the assigned ranks of the brand and competing brands forbenchmarking.

FIGS. 9A-9D exemplarily illustrate screenshots of a sorting interfaceprovided by the brand monitoring platform for sorting social mediainformation into one or more dynamically generated categories. FIG. 9Aexemplarily illustrates a screenshot of the sorting interface displayingcategories dynamically generated by the brand monitoring platform forsorting social media information related to a brand in an aviationindustry. The sorting interface is, for example, hosted on the graphicaluser interface (GUI) provided by the brand monitoring platform. Thesorting interface is configured to display the categories and optionsfor sorting the social media information into the categories. In thisexample, the dynamically generated categories are “related to ABCAirways”, “related to aviation in general”, “related to an event,occasion, person, or place”, and “not related to anything”. The sortinginterface provides a tag element, for example, in the form of a commandbutton for each of the dynamically generated categories. An authorizeduser may click on a command button to tag a post extracted from thesocial media information to a particular category.

Once the brand monitoring platform has developed the categorizationschema for brands in the aviation industry, the brand monitoringplatform allows manual categorization of posts extracted from the socialmedia information for each brand in the aviation industry via thesorting interface. The sorting interface allows an authorized user tosort or tag the social media information. In this example, the socialmedia information comprises an administrator post about a governmentdepartment strike in the public sector that affects all airlines at theHeathrow airport. The authorized user sorts or tags the administratorpost into the category “related to aviation in general”, for example, byclicking on a command button labeled as “related to aviation in general”on the sorting interface as exemplarily illustrated in FIG. 9B.

FIG. 9C exemplarily illustrates a screenshot of the sorting interfacedisplaying dynamically generated categories in another hierarchicallevel. These sub-categories allow another hierarchical level ofcategorization performed by the brand monitoring platform. The sortinginterface displays the sub-categories or a second hierarchical level ofcategories. The sub-categories in the second hierarchical level underthe main category “Related to aviation in general” comprise, forexample, “news in airlines sector”, “travel advice”, “events”, “fact”,“Questions to the fans”, “Travel destination information”, and “Others”.The sorting interface provides a tag element, for example, in the formof a command button for each of the sub-categories. The brand monitoringplatform categorizes the social media information into one or more ofthe sub-categories. Furthermore, the sorting interface provided by thebrand monitoring platform enables a user to tag the social mediainformation to one or more of the sub-categories. In this example, thepost may be tagged as travel advice and news in the airlines sector.Therefore, the brand monitoring platform, based on the inputs receivedvia the sorting interface, categorizes the post under the “traveladvice” sub-category and the “News in airline sector” sub-category byclicking on the corresponding command buttons as exemplarily illustratedin FIG. 9D.

FIG. 10A exemplarily illustrates a table displaying results of acomputation of an audience score for each of multiple brands in anindustry using social media information acquired from a particularsocial media source. Consider an example where the brand monitoringplatform processes social media information related to a brand andcompeting brands in a banking industry for a particular month. The brandmonitoring platform computes measures corresponding to each of theaudience score metric parameters, for example, “number of fans” and “fangrowth”. The intermediate steps performed by the brand monitoringplatform for normalizing each of the measures corresponding to each ofthe audience score metric parameters are disclosed in the detaileddescription of FIG. 13. The weights assigned to the audience scoremetric parameters “number of fans” and “fan growth” are represented incolumns labeled as “fan weight” and “fan growth weight” respectively inthe table exemplarily illustrated in FIG. 10A. The brand monitoringplatform determines the audience score based on a weighted average ofthe normalized measures corresponding to each of the audience scoremetric parameters.

FIG. 10B exemplarily illustrates a table displaying results of acomputation of an engagement score for each of multiple brands in anindustry using social media information acquired from a particularsocial media source. In this example, the brand monitoring platformprocesses social media information related to a brand and competingbrands in a banking industry. The brand monitoring platform computesmeasures corresponding to each of the engagement score metricparameters, for example, “engagement number”, “number of administratorposts”, “percentage of fan posts”, and “net sentiment”. The intermediatesteps performed by the brand monitoring platform for normalizing each ofthe measures corresponding to each of the engagement score metricparameters are disclosed in the detailed description of FIG. 13. Theweights assigned to the engagement score metric parameters “engagementnumber”, “number of administrator posts”, “percentage of fan posts”, and“net sentiment” are represented in columns labeled as “engagement numberweight”, “administrator posts weight”, “fan post weight”, and “netsentiment weight” respectively in the table exemplarily illustrated inFIG. 10B. The brand monitoring platform determines the engagement scorebased on a weighted average of the normalized measures corresponding toeach of the engagement score metric parameters.

FIG. 10C exemplarily illustrates a table displaying results of acomputation of an aggregate score for each of multiple brands in anindustry. The brand monitoring platform retrieves the audience score andthe engagement score determined for each of the brands, for example,from database tables exemplarily illustrated in FIG. 10A and FIG. 10Brespectively, and normalizes each of the audience scores and theengagement scores, as disclosed in the detailed description of FIGS.1A-1B and FIG. 13. Furthermore, the brand monitoring platform assigns anormalized audience score rank and a normalized engagement score rank toeach of the brands based on the normalized audience score and thenormalized engagement score respectively. The normalized audience scorerank and the normalized engagement score rank allow ranking of thebrands in a particular industry. The brand monitoring platform assigns aseparate rank for the audience score and the engagement score of each ofthe brands for analyzing how the brands have performed individually withreference to their quantitative scores against their qualitative scoresrespectively. Assigning a separate normalized audience score rank and anormalized engagement score rank for each of the brands by the brandmonitoring platform enables analysis of strengths and weaknesses of eachof the brands and suggests areas for improvement. The brand monitoringplatform then generates an aggregate score by computing a weightedaverage of the normalized audience score and the normalized engagementscore, normalizes the generated aggregate score, and assigns a brandrank to each of the brands.

FIGS. 11A-11B exemplarily illustrate screenshots of a graphical userinterface (GUI) provided by the brand monitoring platform, displayingaggregate scores generated for multiple brands in particular industries.FIG. 11A exemplarily illustrates normalized aggregate scores generatedby the brand monitoring platform for brands in an aviation industry. Thebrand monitoring platform provides a drop down menu on the GUI forselecting a geographical location within which an aggregate reach of thebrand is to be determined. As exemplarily illustrated in FIG. 11A, thebrand monitoring platform generates a normalized aggregate score of 100for KLM Airlines, a normalized aggregate score of 94 for LufthansaAirlines®, a normalized aggregate score of 91 for Turkish Airlines®, anormalized aggregate score of 86 for Air France®, a normalized aggregatescore of 85 for Southwest Airlines®, a normalized aggregate score of 79for Jet Airways®, etc.

FIG. 11B exemplarily illustrates normalized aggregate scores generatedfor brands in a food/beverage industry. As exemplarily illustrated inFIG. 11B, the brand monitoring platform generates a normalized aggregatescore of 100 for Red Bull®, a normalized aggregate score of 98 forCoca-Cola®, a normalized aggregate score of 91 for Oreo®, a normalizedaggregate score of 87 for Dr Pepper®, a normalized aggregate score of 85for Pringles®, a normalized aggregate score of 84 for Nutella®, etc.

For each of the brands exemplarily illustrated in FIGS. 11A-11B, thebrand monitoring platform uses metric parameters, for example, “numberof likes” and “growth in the number of likes” during the generation ofthe aggregate score for each of the brands. The brand monitoringplatform accounts for the geographical locations where each of thebrands is focused by normalizing the aggregate reach of the brand basedon geography. The brand monitoring platform displays the aggregatescores of each of the brands for a particular month and allows a user toselect other months on the GUI to view the aggregate scores of each ofthe brands for the other months.

FIG. 12 exemplarily illustrates a screenshot of a graphical userinterface (GUI) provided by the brand monitoring platform displaying agraphical representation of a comparative analysis of an audience scoreagainst an engagement score for each of multiple brands in a particularindustry. The graphical representation of the comparative analysisexemplarily illustrated in FIG. 12 is a scatter plot with an X axisrepresenting the audience score and a Y axis representing the engagementscore. The brand monitoring platform provides an analysis of theperformance of a brand in obtaining an audience against the performanceof the brand in engaging the audience. This comparative analysis allowseach of the brands to analyze their relative marketing strengths andweaknesses.

FIG. 13 exemplarily illustrates a flowchart comprising the steps forbenchmarking a brand based on the social media strength of the brand ina particular industry. Consider an example where an airline brand “ABC”requests the brand monitoring platform for benchmarking in the airlinesindustry. The brand monitoring platform identifies competing airlinebrands, for example, KLM, Lufthansa®, Amsterdam Airport Schiphol®,Southwest Airlines®, Turkish Airlines®, AirAsia®, Jet Airways®, AirFrance®, British Airways®, JetBlue Airways®, Cebu Pacific Air, VuelingPeople!, Malaysia Airlines®, Alitalia®, Philippine Airlines, UnitedAirlines®, FlySpiceJet, Qatar Airways®, and Delta Air® Lines. Consideran example where the brand monitoring platform considers the followingcompeting brands: KLM Airlines, Lufthansa Airlines®, and SouthWestAirlines®. The brand monitoring platform retrieves 1301 statistical datafor audience score metric parameters and engagement score metricparameters from a corresponding application programming interface (API)configured for each of the social media sources as disclosed in thedetailed description of FIGS. 1A-1B. In this example, the brandmonitoring platform uses the following statistical data related toaudience score metric parameters for the month of March from an APIconfigured for a social media source such as Facebook®:

Number of fans: KLM—1,335,185, Lufthansa—935,408, SouthWestAirlines—2,158,217.Growth of fans: KLM—237,793, Lufthansa—110,971, SouthWestAirlines—102,367

Number of “People Talking About”: KLM—64,837, Lufthansa—33,834,SouthWest Airlines—33,118 Growth of “People Talking About”: KLM—−11530,Lufthansa—−11506, SouthWest Airlines—−5927

Furthermore, the brand monitoring platform uses the followingstatistical data related to engagement score metric parameters for themonth of March:

KLM (Number of administrator posts—26, with 85,602 likes, 9630 commentsand 7568 shares);Lufthansa—(Number of administrator posts—37, with 39,660 likes, 3459comments and 3830 shares);SouthWest Airlines (Number of administrator posts—21, with 20,941 likes,4501 comments and 1458 shares)

Based on the measures of the engagement score metric parameters, thebrand monitoring platform generates the following engagement number foreach of the competing brands:

KLM—85, Lufthansa—34, SouthWest Airlines—20

Furthermore, the brand monitoring platform tracks the growth of theengagement number as:

KLM—36, Lufthansa—−11, SouthWest Airlines—3

The brand monitoring platform evaluates the other engagement scoremetric parameters, for example:

Number of posts as: KLM—2290 (26 administrator posts and 2264 fanposts), Lufthansa—1030 (37 administrator posts and 993 fan posts),SouthWest Airlines—2150 (21 administrator posts and 2129 fan posts)Growth of posts: KLM—−1437 (2 administrator posts and −1439 fan posts),Lufthansa—−34 (−13 administrator posts and −21 fan posts), SouthWestAirlines—−1070 (−9 administrator posts and −1061 fan posts)

The brand monitoring platform counts the number of posts tagged manuallyin one or more dynamically generated categories for each of the brandsas:

KLM—26 (21 brand related posts, 2 industry related posts, 2 currentaffairs related posts, 1 other post)Lufthansa—37 (25 brand related posts, 6 industry related posts, 1current affairs related post, 1 other post)SouthWest Airlines—21 (20 administrator posts, 1 industry related post)

The brand monitoring platform tracks the growth of posts in the brandcategory as:

KLM—+5 Brand posts, Lufthansa—−1 Brand post, SouthWest Airlines—−9 Brandposts

The brand monitoring platform tracks the number of posts orderedaccording to the sentiment of the posts as:

KLM—2286 (635 positive, 113 negative, 1538 neutral posts),Lufthansa—1027 (205 positive, 53 negative, 769 neutral posts), SouthWestAirlines—2142 (866 positive, 171 negative, 1105 neutral posts)

The brand monitoring platform computes the number of posts that have apositive sentiment or a negative sentiment. The number of posts with apositive sentiment and a negative sentiment are used to calculateintermediate measures. For example, an intermediate measure of themetric parameter, net sentiment, is computed as:

Net sentiment=positive sentiment−negative sentiment.

The brand monitoring platform assigns equal weights to both positivesentiments and negative sentiments. The brand monitoring platform shiftsthe net sentiment to ensure that differential values are positive. Thebrand monitoring platform then normalizes the net sentiment, forexample, by normalizing the variance in the net sentiment to ensure thatthe measure of the metric parameter is in line with the other measures.

The brand monitoring platform determines the growth in the number ofposts ordered according to the sentiment of the posts as follows:

KLM—1441 (−541 positive, −103 negative), Lufthansa—−37 (−18 positive, 11negative),SouthWest Airlines—1078 (−307 positive, −87 negative)

The brand monitoring platform normalizes 1302 the statistical data foreach of the metric parameters, that is, the audience score metricparameters and the engagement score metric parameters to reducestatistical differences and outlier data. The brand monitoring platformnormalizes measures corresponding to each of the metric parameters bycalculating statistics based on the metric parameters of all the brands.For example, the brand monitoring platform determines a median value forthe audience score metric parameter “number of fans” to normalize thenumber of fans to a standard value. Furthermore, the brand monitoringplatform computes an inter-quartile range to normalize standarddeviations. The brand monitoring platform performs a sequence of stepsas follows: The brand monitoring platform computes the relevantmeasures, for example, a percentage growth of fans. The brand monitoringplatform then computes intermediate values for normalization of themeasures of one or more audience score metric parameters and engagementscore metric parameters. For example, the brand monitoring platformcomputes a square root of the number of fans in order to reduce spreadof a measure between extreme values.

The brand monitoring platform then normalizes 1303 the statistical datafor each of the metric parameters, that is, the audience score metricparameters and/or the engagement score metric parameters according tothe geographical locations of the brands. In this example, the brandmonitoring platform normalizes the measures of one or more audiencescore metric parameters and/or one or more engagement score metricparameters that vary according to the geographical location of the brandsuch that the median value of the measures for a particular audiencescore metric parameter or a particular engagement score metric parameterfor the brand is 100. The brand monitoring platform normalizes the othermeasures such that the median value is 100. The brand monitoringplatform normalizes geography specific metric parameters according tothe geographical location and reach of the brand in the particulargeographical location. For example, for the metric parameters that aregeography specific, the brand monitoring platform normalizes themeasures corresponding to each of the metric parameters for the brandsin the Asia Pacific region, the North America region, the Europe region,etc., separately. The brand monitoring platform tapers extreme values ofmeasures recorded for each of the audience score metric parametersand/or the engagement score metric parameters. For example, the brandmonitoring platform tapers values that are three times an inter-quartilerange more than a third quartile. These values are tapered to be amaximum of double the value.

Consider an example where the brand monitoring platform considers the“number of fans” metric parameter. The brand monitoring platformnormalizes the measures corresponding to this metric parameter by firstapplying a square root of each of the measures for this metric parameterto obtain a new statistical distribution. The normalized measure, thatis, the square root of the measure for the “number of fans” metricparameter derived from the original measure is known as an intermediatevalue. The brand monitoring platform then computes a mean value of thenew statistical distribution as 172.15, the standard deviation as138.63, the inter-quartile range as 121.3, and the third quartile as189.6. The brand monitoring platform tapers the measures based on athreshold value that is derived as: (189.6+3*121.3=553.5) to a maximumvalue of 1107. Therefore, extreme values of the normalized measures ofeach of the brands corresponding to the “number of fans” metricparameter that exceed the threshold value 553.5 by a large value aretapered to 1107. The brand monitoring platform normalizes the measuresthat exceed the threshold value of 553.5 by a small value, for example,to a measure closer to or equal to 553.5.

The brand monitoring platform normalizes the individual measures of eachof the brands corresponding to the “number of fans” metric parameter asfollows:

KLM Airlines—532.1501

The brand monitoring platform computes the square root of the measure ofthe number of fans for KLM Airlines as: √1335185=1155.502. The brandmonitoring platform then normalizes the intermediate value, that is, thesquare root of the measure of the number of fans, using the medianvalue, the standard deviation value, and the threshold value derived forthe complete statistical distribution taken from the above descriptionas: 1155.502/1.7215=671.2181, 172.15+(671.2181-172.15)/1.3863=532.1501.Since this normalized measure of 532.1501 does not exceed the thresholdcomputed for the distribution, the brand monitoring platform does nottaper this measure.

Lufthansa Airlines—453.233

The brand monitoring platform computes the square root of the measure ofthe number of fans for Lufthansa Airlines as: √935408=967.1649. Thebrand monitoring platform then normalizes the intermediate value, thatis, the square root of the measure of the number of fans, using themedian value, the standard deviation value and the threshold valuederived for the complete statistical distribution taken from the abovedescription as: 967.1649, 967.1649/1.7215=561.815,172.15+(561.815-172.15)/1.3863=453.233. Since this normalized measure of453.233 does not exceed the threshold value computed for thedistribution, the brand monitoring platform does not taper this measure.

SouthWest Airlines—580.483

The brand monitoring platform computes the square root of the measure ofthe number of fans for Lufthansa Airlines as: √2158217=1469.087. Thebrand monitoring platform then normalizes the intermediate value, thatis, the square root of the measure of the number of fans, using themedian value, the standard deviation value and the threshold valuederived for the complete statistical distribution taken from the abovedescription as: 1469.087/1.7215=853.376,172.15+(853.376-172.15)/1.3863=663.549. Since this normalized measure of663.549 exceeds the threshold value computed for the distribution, thebrand monitoring platform tapers this measure to a measure closer to thethreshold value, for example, 580.483.

The brand monitoring platform then normalizes the measures correspondingto one or more audience score metric parameters and/or one or moreengagement score metric parameters such that the standard deviation is100. Subsequent to performing the computational steps detailed above forthe other audience score metric parameters and engagement score metricparameters, the brand monitoring platform obtains the followingnormalized results:

Growth of fans: KLM—243.5084, Lufthansa—180.0342, SouthWestAirlines—87.5184

Number of “People Talking About”: KLM—480.2132, Lufthansa—293.9322,SouthWest Airlines—292.8419 Growth of “People Talking About”:KLM—38.1762, Lufthansa—37.0217, SouthWest Airlines—87.4053

Engagement number: KLM—169.4406, Lufthansa—80.4079, SouthWestAirlines—84.3358Growth of engagement number: KLM—318.2043, Lufthansa—79.3501, SouthWestAirlines—162.1752Number of administrator posts: KLM—587.5118, Lufthansa—602.3587,SouthWest Airlines—264.1188Percentage of fan posts: KLM—218.8007, Lufthansa—211.1232, SouthWestAirlines—291.5611Number of posts in a manual category: KLM—219.9026, Lufthansa—152.2517,SouthWest Airlines—312.2309Net positive posts by sentiment: KLM—269.0621, Lufthansa—192.0146,SouthWest Airlines—104.8804

The brand monitoring platform determines 1304 an audience score for eachbrand using a weighted average of the normalized measures of theaudience score metric parameters. The audience score metric parameterscomprise the quantitative metrics. The brand monitoring platform assignsweights to the audience score metric parameters based on the industry.In this example, the brand monitoring platform assigns weights to theaudience score metric parameters as follows:

Number of fans—65%, Growth of fans—20%, Number of social media usersdiscussing the brand, referred to as “Talking About This”—10%, Growth in“Talking About This”—5%

The brand monitoring platform therefore determines the audience scorefor KLM as:0.65×532.1501+0.20×243.5084+0.1×480.2132+0.05×38.1762=444.529

The brand monitoring platform determines the audience score forLufthansa Airlines and SouthWest Airlines as:

Lufthansa—361.852 (computed as0.65×453.233+0.20×180.0342+0.1×293.9322+0.05×37.0217=361.852)SouthWest Airlines—428.472 (computed as0.65×580.483+0.20×87.5184+0.1×292.8419+0.05×87.4053=428.46)

The brand monitoring platform determines 1305 an engagement score foreach brand using a weighted average of the normalized measures of theengagement score metric parameters, that is, the qualitative metrics. Inthis example, the brand monitoring platform assigns the followingweights to the engagement score metric parameters for determining theengagement score: 35% for the engagement number, 15% for growth in theengagement number, 20% for the number of administrator posts, 5% forpercentage of fan posts, 15% for number of posts manually sorted intothe dynamically generated categories, and 10% for net positivesentiment.

The brand monitoring platform obtains the engagement score for each ofthe brands as follows:

For KLM Airlines, the engagement score is 295.3688.(0.35×169.4406+0.15×318.2043+0.20×587.5118+0.05×218.8007+0.15×219.9026+0.1×269.0621=295.3688)For Lufthansa Airlines, the engagement score is 213.1123.(0.35×80.4079+0.15×79.3501+0.20×602.3587+0.05×211.1232+0.15×152.2517+0.1×192.0146=213.1123)For SouthWest Airlines, the engagement score is 178.5683(0.35×84.3358+0.15×162.1752+0.20×264.1188+0.05×291.5611+0.15×312.2309+0.1×104.8804=178.5683)

The brand monitoring platform normalizes 1306 the audience score and theengagement score such that the mean and the variance for each of theaudience score and the engagement score is the same. Therefore, thebrand monitoring platform determines the normalized audience score foreach of the brands as follows:

KLM—537.332 (444.529/0.848=524.209, 84.8+(524.209-84.8)/0.971=537.332)Lufthansa—436.924 (361.852/0.848=426.712,84.8+(426.712-84.8)/0.971=436.924) SouthWest Airlines—517.832(428.472/0.848=505.274, 84.8+(505.274-84.8)/0.971=517.832)

The mean value for the normalized distribution for the engagement scoreacross different brands, in this example, is 113, and the standarddeviation for the normalized distribution for the engagement score is91.7. The brand monitoring platform determines the engagement score foreach of the brands as follows:

KLM Airlines—274.8193 (295.3688/1.13=261.3883,113+(261.3883-113)/0.917=274.8193) Lufthansa Airlines—195.4373(213.1123/1.13=188.595, 113+(188.595-113)/0.917=195.4373) SouthWestAirlines—162.1004 (178.5683/1.13=158.025,113+(158.025-113)/0.917=162.1003)

The brand monitoring platform generates 1307 an aggregate score as theweighted average of the audience score and the engagement score. In thisexample, the weighs assigned to the audience score and the engagementscore are equal. Therefore, the aggregate score for each of the brandsis computed as follows:

KLM Airlines—406.076 ((537.3326+274.8193)/2), LufthansaAirlines—316.1809 ((436.9245+195.4373)/2) SouthWest Airlines—339.966((517.832+162.1004)/2)

The brand monitoring platform normalizes the aggregate score between 0and 100. On normalizing the aggregate score, the brand monitoringplatform obtains the following values:

Aggregate score for KLM—100 (406.076*100/406.076=100)Aggregate score for Lufthansa Airlines—78 (316.1809*100/406.076=78)Aggregate score for SouthWest Airlines—84 (339.966*100/406.076=84)

The brand monitoring platform assigns 1307 a rank to each of the threeairline brands as:

KLM—1, Lufthansa—3, SouthWest Airlines—2

Similarly, the brand monitoring platform performs the steps 1301 to 1307for the airline brand “ABC” and benchmarks the airline brand ABC in theairlines industry based on the social media strength of the brand ABC incomparison with the competing brands, for example, KLM, Lufthansa, andSouthWest Airlines in the virtual social media environment.

FIG. 14 illustrates a computer implemented system 1400 for benchmarkinga brand based on the social media strength of the brand. The computerimplemented system 1400 disclosed herein comprises a brand monitoringplatform 1401 accessible by a computing device 1411 via a network 1410.The brand monitoring platform 1401 comprises at least one processorconfigured to execute modules 1402, 1403, 1404, 1405, 1406, 1407, 1409,etc., of the brand monitoring platform 1401 for monitoring the brand ina virtual social media environment. The brand monitoring platform 1401is, for example, hosted on a server that is accessible via the network1410. In an embodiment, the brand monitoring platform 1401 provides aweb application or a mobile application that can be installed on acomputing device 1411 by a user. The computing device 1411 is, forexample, a laptop, a tablet computer, a mobile phone, a personalcomputer, a personal digital assistant, etc.

The brand monitoring platform 1401 comprises an information acquisitionmodule 1402, an industry identification module 1403, a categorygeneration module 1404, a sorting module 1406, a scoring module 1407, aconfiguration module 1405, and a brand information database 1408.Furthermore, the brand monitoring platform 1401 provides a graphicaluser interface (GUI) 1409 for acquiring input information on a brand,for acquiring categories from one or more users for sorting social mediainformation acquired from one or more social media sources, fordisplaying an aggregate score and a brand rank received by a particularbrand in relation to all the competing brands in a particular industry,etc. The GUI 1409 comprises a set of web pages hosted on a serverassociated with the brand monitoring platform 1401.

The information acquisition module 1402 acquires input information onthe brand, for example, from one or more online resources, inputsprovided by the user via the GUI 1409, etc. The industry identificationmodule 1403 identifies industries related to the brand and competingbrands in the identified industries using the acquired input informationon the brand. The information acquisition module 1402 acquires socialmedia information related to the brand and the competing brands in theidentified industries from multiple social media sources in the virtualsocial media environment via a network 1410. The network 1410 is, forexample, the internet, an intranet, a local area network, a wide areanetwork, a communication network implementing Wi-Fi® of the WirelessEthernet Compatibility Alliance, Inc., a cellular network, a mobilecommunication network such as a global system for mobile communications(GSM) network, a general packet radio service (GPRS) network, etc.

The category generation module 1404 dynamically generates categories,for example, a location of each of the identified industries related tothe brand and each of the competing brands, a location of each ofmultiple authors of the social media information, types of social mediasources utilized by the brand and each of the competing brands,marketing elements, etc., in one or more hierarchical levels in each ofthe identified industries based on an independent analysis of theacquired social media information related to the brand and the competingbrands from each of the social media sources. In an embodiment, thecategory generation module 1404 determines clusters of similar contentportions from the acquired social media information and identifies oneor more common categories applicable to the brand and each of thecompeting brands in each of the identified industries from thedetermined clusters of the similar content portions during theindependent analysis of the acquired social media information related tothe brand and the competing brands from each of the social media sourcesfor the dynamic generation of the categories.

The sorting module 1406 sorts the acquired social media informationrelated to the brand and the competing brands in each of the identifiedindustries into one or more dynamically generated categories in one ormore of the hierarchical levels using a sorting interface 1409 a. Thesorting interface 1409 a is hosted on the GUI 1409. In an embodiment,the sorting module 1406 acquires inputs for sorting of the acquiredsocial media information related to the brand and the competing brandsin each of the identified industries into one or more of the dynamicallygenerated categories in one or more hierarchical levels from a user viathe sorting interface 1409 a.

The configuration module 1405 configures one or more of the weightedaudience score metric parameters and one or more of the weightedengagement score metric parameters for determination of the audiencescore and the engagement score respectively, based on predeterminedcriteria. The scoring module 1407 determines an audience score for thebrand and each of the competing brands by measuring an aggregate reachof the brand and each of the competing brands in the virtual socialmedia environment based on one or more of multiple weighted audiencescore metric parameters using the sorted social media information asdisclosed in the detailed description of FIGS. 1A-1B and FIG. 6. In anembodiment, the scoring module 1407 normalizes measures corresponding toeach of the audience score metric parameters. The scoring module 1407assigns individual weights to the audience score metric parameters. Thescoring module 1407 determines a weighted average of the normalizedmeasures corresponding to each of the audience score metric parametersusing the assigned individual weights for determination of the audiencescore for the brand and each of the competing brands.

The scoring module 1407 determines an engagement score for the brand andeach of the competing brands by measuring interaction between the brandand each of the competing brands with their corresponding followersbased on one or more of multiple weighted engagement score metricparameters using the sorted social media information as disclosed in thedetailed description of FIGS. 1A-1B and FIG. 7. In an embodiment, thescoring module 1407 determines the engagement score for the brand andeach of the competing brands by normalizing measures corresponding toeach of the engagement score metric parameters. The scoring module 1407assigns individual weights to the engagement score metric parameters.The scoring module 1407 determines a weighted average of the normalizedmeasures corresponding to each of the engagement score metric parametersusing the assigned individual weights for the determination of theengagement score for the brand and each of the competing brands.

In an embodiment, the scoring module 1407 normalizes measurescorresponding to one or more audience score metric parameters and one ormore engagement score metric parameters, based on a location of each ofthe identified industries related to the brand and each of the competingbrands during the determination of the audience score and saidengagement score respectively, for the brand and each of the competingbrands, for reducing statistical differences in the measures triggeredby a difference of the location of each of the identified industriesrelated to the brand and the competing brands. In an embodiment, thescoring module 1407 normalizes measures corresponding to each of theweighted audience score metric parameters for removing statisticaldifferences between extreme measures corresponding to each of theweighted audience score metric parameters, and for reducing outlierdata. In an embodiment, the scoring module 1407 normalizes measurescorresponding to each of the weighted engagement score metric parametersfor removing statistical differences between extreme measurescorresponding to each of the weighted engagement score metricparameters, and for reducing outlier data.

The scoring module 1407 generates an aggregate score for the brand andeach of the competing brands using the determined audience score and thedetermined engagement score. In an embodiment, the scoring module 1407generates the aggregate score for the brand and each of the competingbrands by determining a weighted average of the determined audiencescore and the determined engagement score. Furthermore, the scoringmodule 1407 assigns a rank to the brand and each of the competing brandsbased on the aggregate score for benchmarking the brand based on thesocial media strength of the brand in comparison with the competingbrands in the virtual social media environment. The brand informationdatabase 1408 is, for example, a component of a relational databasemanagement system that stores the input information on the brands, thesocial media information acquired from multiple social media sources,statistical information mapped to audience score metric parameters andengagement score metric parameters, the audience score and theengagement score for each of the brands across multiple industries, theaggregate score and the brand rank of each of the brands, etc. In anembodiment, the brand monitoring platform 1401 periodically tracks andupdates changes to the social media information in the brand informationdatabase 1408, and updates the aggregate score and the brand rank foreach of the brands in a particular industry and/or in a particulargeographical location.

FIG. 15 exemplarily illustrates the architecture of a computer system1500 employed by the brand monitoring platform 1401 for benchmarking abrand based on the social media strength of the brand. The brandmonitoring platform 1401 of the computer implemented system 1400exemplarily illustrated in FIG. 14 employs the architecture of thecomputer system 1500 exemplarily illustrated in FIG. 15.

The brand monitoring platform 1401 communicates with a computing device1411 of a user authorized to update the brand information database 1408in the brand monitoring platform 1401 via a network 1410. The network1410 is, for example, a short range network or a long range network. Thenetwork 1410 is, for example, the internet, a local area network, a widearea network, a wireless network, a mobile communication network, etc.The computer system 1500 comprises, for example, a processor 1501, amemory unit 1502 for storing programs and data, an input/output (I/O)controller 1503, a network interface 1504, a data bus 1505, a displayunit 1506, input devices 1507, a fixed media drive 1508, a removablemedia drive 1509 for receiving removable media, output devices 1510,etc.

The processor 1501 is an electronic circuit that executes computerprograms. The memory unit 1502 is used for storing computer programs,applications, and data. For example, the information acquisition module1402, the industry identification module 1403, the category generationmodule 1404, the sorting module 1406, the scoring module 1407, theconfiguration module 1405, etc., of the brand monitoring platform 1401are stored in the memory unit 1502 of the computer system 1500 of thebrand monitoring platform 1401. The memory unit 1502 is, for example, arandom access memory (RAM) or another type of dynamic storage devicethat stores information and instructions for execution by the processor1501. The memory unit 1502 also stores temporary variables and otherintermediate information used during execution of instructions by theprocessor 1501. The computer system 1500 further comprises a read onlymemory (ROM) or another type of static storage device that stores staticinformation and instructions for the processor 1501.

The network interface 1504 enables connection of the computer system1500 to the network 1410. For example, the brand monitoring platform1401 connects to the network 1410 via the network interface 1504. Thenetwork interface 1504 comprises, for example, an infrared (IR)interface, an interface implementing Wi-Fi® of the Wireless EthernetCompatibility Alliance, Inc., a universal serial bus (USB) interface, alocal area network (LAN) interface, a wide area network (WAN) interface,etc. The I/O controller 1503 controls input actions, for example, manualtagging actions, and output actions performed by the brand monitoringplatform 1401. The data bus 1505 permits communications between themodules, for example, 1402, 1403, 1404, 1405, 1406, 1407, 1409, etc., ofthe brand monitoring platform 1401.

The display unit 1506 of the brand monitoring platform 1401, via thegraphical user interface (GUI) 1409, displays information, for example,the aggregate score and the brand rank of a particular brand and each ofthe competing brands in an industry. Furthermore, the display unit 1506of the brand monitoring platform 1401 displays the hierarchical levelsof categories dynamically generated by the brand monitoring platform1401 and sorting interfaces 1409 a for manually tagging one or moreposts from the social media information to the categories andsub-categories generated by the brand monitoring platform 1401. Theinput devices 1507 are used for inputting data into the computer system1500. The user uses the input devices 1507 to provide inputs to thebrand monitoring platform 1401. For example, a user may drag and drop aparticular post collected as part of the social media information to oneor more categories dynamically generated by the brand monitoringplatform 1401 via the sorting interface 1409 a. The input devices 1507are, for example, a keyboard such as an alphanumeric keyboard, ajoystick, a pointing device such as a computer mouse, a touch pad, alight pen, etc.

The output devices 1510 output the results of operations performed bythe brand monitoring platform 1401. For example, the brand monitoringplatform 1401 notifies changes to the aggregate score or the brand rankof each of the brands in a particular industry on receiving updates tothe brand information over a predetermined duration of time, to all thebrand owners and brand communities associated with each of the brandsthrough a display notification on the GUI 1409 of the brand monitoringplatform 1401.

Computer applications and computer programs are used for operating thecomputer system 1500. The computer programs are loaded onto the fixedmedia drive 1508 and into the memory unit 1502 of the computer system1500 via the removable media drive 1509. In an embodiment, the computerapplications and the computer programs may be loaded directly into thecomputer system 1500 via the network 1410. Computer applications andcomputer programs are executed by double clicking a related icondisplayed on the display unit 1506 using one of the input devices 1507.

The computer system 1500 employs an operating system for performingmultiple tasks. The operating system is responsible for management andcoordination of activities and sharing of resources of the computersystem 1500. The operating system further manages security of thecomputer system 1500, peripheral devices connected to the computersystem 1500, and network connections. The operating system employed onthe computer system 1500 recognizes, for example, inputs provided by auser using one of the input devices 1507, the output display, files, anddirectories stored locally on the fixed media drive 1508, for example, ahard drive. The operating system on the computer system 1500 executesdifferent computer programs using the processor 1501.

The processor 1501 retrieves the instructions for executing the modules,for example, 1402, 1403, 1404, 1405, 1406, 1407, etc., of the brandmonitoring platform 1401 from the memory unit 1502. A program counterdetermines the location of the instructions in the memory unit 1502. Theprogram counter stores a number that identifies the current position inthe computer program of each the modules, for example, 1402, 1403, 1404,1405, 1406, 1407, etc., of the brand monitoring platform 1401.

The instructions fetched by the processor 1501 from the memory unit 1502after being processed are decoded. The instructions are placed in aninstruction register in the processor 1501. After processing anddecoding, the processor 1501 executes the instructions. For example, theinformation acquisition module 1402 defines instructions for acquiringinput information on the brand. The industry identification module 1403defines instructions for identifying industries related to the brand andcompeting brands in the identified industries using the acquired inputinformation on the brand. The information acquisition module 1402defines instructions for acquiring social media information related tothe brand and the competing brands in the identified industries frommultiple social media sources in the virtual social media environmentvia a network 1410.

The category generation module 1404 defines instructions for dynamicallygenerating categories in one or more hierarchical levels in each of theidentified industries based on an independent analysis of the acquiredsocial media information related to the brand and competing brands fromeach of the social media sources. In an embodiment, the categorygeneration module 1404 defines instructions for determining clusters ofsimilar content portions from the acquired social media information andfor identifying one or more common categories applicable to the brandand each of the competing brands in each of the identified industriesfrom the determined clusters of similar content portions during theindependent analysis of the acquired social media information related tothe brand and the competing brands from each of the social media sourcesfor dynamic generation of the categories.

The sorting module 1406 defines instructions for sorting the acquiredsocial media information related to the brand and the competing brandsin each of the identified industries into one or more dynamicallygenerated categories in one or more hierarchical levels using thesorting interface 1409 a. Furthermore, the sorting module 1406 definesinstructions for acquiring inputs for sorting of the acquired socialmedia information related to the brand and the competing brands in eachof the identified industries into one or more of the dynamicallygenerated categories in one or more hierarchical levels from a user viathe sorting interface 1409 a.

The configuration module 1405 defines instructions for configuring oneor more weighted audience score metric parameters and one or moreweighted engagement score metric parameters for the determination of theaudience score and the engagement score respectively, based onpredetermined criteria. The scoring module 1407 defines instructions fordetermining an audience score for the brand and each of the competingbrands by measuring an aggregate reach of the brand and each of thecompeting brands in the virtual social media environment based on one ormore weighted audience score metric parameters using the sorted socialmedia information. Furthermore, the scoring module 1407 definesinstructions for normalizing measures corresponding to each of theaudience score metric parameters, for assigning individual weights tothe audience score metric parameters, and for determining a weightedaverage of the normalized measures corresponding to each of the audiencescore metric parameters using the assigned individual weights fordetermining the audience score for the brand and each of the competingbrands.

The scoring module 1407 defines instructions for determining anengagement score for the brand and each of the competing brands bymeasuring interaction between the brand and each of the competing brandsand their followers based on one or more weighted engagement scoremetric parameters using the sorted social media information. The scoringmodule 1407 defines instructions for normalizing measures correspondingto each of the engagement score metric parameters, for assigningindividual weights to the engagement score metric parameters, and fordetermining a weighted average of the normalized measures correspondingto each of the engagement score metric parameters using the assignedindividual weights for determining the engagement score for the brandand each of the competing brands.

Furthermore, the scoring module 1407 defines instructions fornormalizing measures corresponding to one or more audience score metricparameters and one or more engagement score metric parameters, based ona location of each of the identified industries related to the brand andeach of the competing brands during the determination of the audiencescore and the engagement score respectively, for the brand and each ofthe competing brands, for reducing statistical differences in themeasures triggered by a difference of the location of each of theidentified industries related to the brand and each of the competingbrands. The scoring module 1407 defines instructions for normalizingmeasures corresponding to each of the weighted audience score metricparameters for reducing statistical differences between extreme measurescorresponding to each of the weighted audience score metric parametersand for reducing outlier data. The scoring module 1407 definesinstructions for normalizing measures corresponding to each of theweighted engagement score metric parameters for reducing statisticaldifferences between extreme measures corresponding to each of theweighted engagement score metric parameters and for reducing outlierdata.

The scoring module 1407 defines instructions for generating an aggregatescore for the brand and each of the competing brands using thedetermined audience score and the determined engagement score. Thescoring module 1407 defines instructions for determining a weightedaverage of the determined audience score and the determined engagementscore for generating the aggregate score for the brand and each of thecompeting brands. Furthermore, the scoring module 1407 definesinstructions for assigning a rank to the brand and each of the competingbrands based on the aggregate score for benchmarking of the brand basedon the social media strength of the brand in comparison with thecompeting brands in the virtual social media environment.

The processor 1501 of the computer system 1500 employed by the brandmonitoring platform 1401 retrieves the instructions defined by theinformation acquisition module 1402, the industry identification module1403, the category generation module 1404, the sorting module 1406, thescoring module 1407, and the configuration module 1405 of the brandmonitoring platform 1401, and executes the instructions. At the time ofexecution, the instructions stored in the instruction register areexamined to determine the operations to be performed. The processor 1501then performs the specified operations. The operations comprisearithmetic operations and logic operations. The operating systemperforms multiple routines for performing a number of tasks required toassign the input devices 1507, the output devices 1510, and memory forexecution of the modules, for example, 1402, 1403, 1404, 1405, 1406,1407, etc., of the brand monitoring platform 1401. The tasks performedby the operating system comprise, for example, assigning memory to themodules, for example, 1402, 1403, 1404, 1405, 1406, 1407, etc., of thebrand monitoring platform 1401, and to data used by the brand monitoringplatform 1401, moving data between the memory unit 1502 and disk units,and handling input/output operations. The operating system performs thetasks on request by the operations and after performing the tasks, theoperating system transfers the execution control back to the processor1501. The processor 1501 continues the execution to obtain one or moreoutputs. The outputs of the execution of the modules, for example, 1402,1403, 1404, 1405, 1406, 1407, etc., of the brand monitoring platform1401 are displayed to the user on the display unit 1506.

For purposes of illustration, the detailed description refers to thebrand monitoring platform 1401 being run locally on a computer system1500; however the scope of the computer implemented method and system1400 disclosed herein is not limited to the brand monitoring platform1401 being run locally on the computer system 1500 via the operatingsystem and the processor 1501, but may be extended to run remotely overthe network 1410 by employing a web browser and a remote server, amobile phone, or other electronic devices.

Disclosed herein is also a computer program product comprising anon-transitory computer readable storage medium that stores computerprogram codes comprising instructions executable by at least oneprocessor 1501 for benchmarking a brand based on the social mediastrength of the brand. As used herein, the term “non-transitory computerreadable storage medium” refers to all computer readable media, forexample, non-volatile media such as optical disks or magnetic disks,volatile media such as a register memory, a processor cache, etc., andtransmission media such as wires that constitute a system bus coupled tothe processor 1501, except for a transitory, propagating signal.

The computer program product disclosed herein comprises one or morecomputer program codes for benchmarking a brand based on the socialmedia strength of the brand. The computer program codes comprise a firstcomputer program code for acquiring input information on a brand; asecond computer program code for identifying industries related to thebrand and competing brands in the identified industries using theacquired input information on the brand; a third computer program codefor acquiring social media information related to the brand and thecompeting brands in the identified industries from multiple social mediasources in the virtual social media environment via a network 1410; afourth computer program code for dynamically generating categories inone or more hierarchical levels in each of the identified industriesbased on an independent analysis of the acquired social mediainformation related to the brand and the competing brands from each ofthe social media sources; a fifth computer program code for sorting theacquired social media information related to the brand and the competingbrands in each of the identified industries into one or more of thedynamically generated categories in one or more hierarchical levelsusing the sorting interface 1409 a; a sixth computer program code fordetermining an audience score for the brand and each of the competingbrands by measuring an aggregate reach of the brand and each of thecompeting brands in the virtual social media environment based on one ormore of multiple weighted audience score metric parameters using thesorted social media information; a seventh computer program code fordetermining an engagement score for the brand and each of the competingbrands by measuring interaction between the brand and each of thecompeting brands and their corresponding followers based on one or moreof multiple weighted engagement score metric parameters using the sortedsocial media information; and an eighth computer program code forgenerating an aggregate score for the brand and each of the competingbrands using the determined audience score and the determined engagementscore. The computer program product disclosed herein further comprisesadditional computer program codes for performing additional steps thatmay be required and contemplated for benchmarking the brand based on thesocial media strength of the brand. In an embodiment, a single piece ofcomputer program code comprising computer executable instructionsperforms one or more steps of the computer implemented method disclosedherein for benchmarking the brand based on the social media strength ofthe brand.

The computer program codes comprising computer executable instructionsare embodied on the non-transitory computer readable storage medium. Theprocessor 1501 of the computer system 1500 retrieves these computerexecutable instructions and executes them. When the computer executableinstructions are executed by the processor 1501, the computer executableinstructions cause the processor 1501 to perform the steps of thecomputer implemented method for benchmarking the brand based on thesocial media strength of the brand in comparison with the competingbrands in the virtual social media environment.

It will be readily apparent that the various methods and algorithmsdisclosed herein may be implemented on computer readable mediaappropriately programmed for general purpose computers and computingdevices. As used herein, the term “computer readable media” refers tonon-transitory computer readable media that participate in providingdata, for example, instructions that may be read by a computer, aprocessor or a like device. Non-transitory computer readable mediacomprise all computer readable media, for example, non-volatile media,volatile media, and transmission media, except for a transitory,propagating signal. Non-volatile media comprise, for example, opticaldisks or magnetic disks and other persistent memory volatile mediaincluding a dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Volatile media comprise, for example, aregister memory, a processor cache, a random access memory (RAM), etc.Transmission media comprise, for example, coaxial cables, copper wireand fiber optics, including wires that constitute a system bus coupledto a processor. Common forms of computer readable media comprise, forexample, a floppy disk, a flexible disk, a hard disk, magnetic tape, anyother magnetic medium, a compact disc-read only memory (CD-ROM), adigital versatile disc (DVD), any other optical medium, punch cards,paper tape, any other physical medium with patterns of holes, a randomaccess memory (RAM), a programmable read only memory (PROM), an erasableprogrammable read only memory (EPROM), an electrically erasableprogrammable read only memory (EEPROM), a flash memory, any other memorychip or cartridge, or any other medium from which a computer can read. A“processor” refers to any one or more microprocessors, centralprocessing unit (CPU) devices, computing devices, microcontrollers,digital signal processors or like devices. Typically, a processorreceives instructions from a memory or like device and executes thoseinstructions, thereby performing one or more processes defined by thoseinstructions. Further, programs that implement such methods andalgorithms may be stored and transmitted using a variety of media, forexample, the computer readable media in a number of manners. In anembodiment, hard-wired circuitry or custom hardware may be used in placeof, or in combination with, software instructions for implementation ofthe processes of various embodiments. Therefore, the embodiments are notlimited to any specific combination of hardware and software. Ingeneral, the computer program codes comprising computer executableinstructions may be implemented in any programming language. Someexamples of languages that can be used comprise C, C++, C#, Perl,Python, or JAVA. The computer program codes or software programs may bestored on or in one or more mediums as object code. The computer programproduct disclosed herein comprises computer executable instructionsembodied in a non-transitory computer readable storage medium, whereinthe computer program product comprises computer program codes forimplementing the processes of various embodiments.

Where databases are described such as the brand information database1408, it will be understood by one of ordinary skill in the art that (i)alternative database structures to those described may be readilyemployed, and (ii) other memory structures besides databases may bereadily employed. Any illustrations or descriptions of any sampledatabases disclosed herein are illustrative arrangements for storedrepresentations of information. Any number of other arrangements may beemployed besides those suggested by tables illustrated in the drawingsor elsewhere. Similarly, any illustrated entries of the databasesrepresent exemplary information only; one of ordinary skill in the artwill understand that the number and content of the entries can bedifferent from those disclosed herein. Further, despite any depiction ofthe databases as tables, other formats including relational databases,object-based models, and/or distributed databases may be used to storeand manipulate the data types disclosed herein. Likewise, object methodsor behaviors of a database can be used to implement various processessuch as those disclosed herein. In addition, the databases may, in aknown manner, be stored locally or remotely from a device that accessesdata in such a database. In embodiments where there are multipledatabases in the system, the databases may be integrated to communicatewith each other for enabling simultaneous updates of data linked acrossthe databases, when there are any updates to the data in one of thedatabases.

The present invention can be configured to work in a network environmentincluding a computer that is in communication with one or more devicesvia a communication network. The computer may communicate with thedevices directly or indirectly, via a wired medium or a wireless mediumsuch as the Internet, a local area network (LAN), a wide area network(WAN) or the Ethernet, token ring, or via any appropriate communicationsmeans or combination of communications means. Each of the devices maycomprise computers such as those based on the Intel® processors, AMD®processors, UltraSPARC® processors, IBM® processors, etc., that areadapted to communicate with the computer. Any number and type ofmachines may be in communication with the computer.

The foregoing examples have been provided merely for the purpose ofexplanation and are in no way to be construed as limiting of the presentinvention disclosed herein. While the invention has been described withreference to various embodiments, it is understood that the words, whichhave been used herein, are words of description and illustration, ratherthan words of limitation. Further, although the invention has beendescribed herein with reference to particular means, materials, andembodiments, the invention is not intended to be limited to theparticulars disclosed herein; rather, the invention extends to allfunctionally equivalent structures, methods and uses, such as are withinthe scope of the appended claims. Those skilled in the art, having thebenefit of the teachings of this specification, may affect numerousmodifications thereto and changes may be made without departing from thescope and spirit of the invention in its aspects.

We claim:
 1. A computer implemented method for benchmarking a brandbased on social media strength of said brand, comprising: providing abrand monitoring platform comprising at least one processor configuredto monitor said brand in a virtual social media environment; acquiringinput information on said brand by said brand monitoring platform;identifying industries related to said brand and competing brands insaid identified industries using said acquired input information on saidbrand by said brand monitoring platform; acquiring social mediainformation related to said brand and said competing brands in saididentified industries from a plurality of social media sources in saidvirtual social media environment by said brand monitoring platform via anetwork; dynamically generating categories in one or more hierarchicallevels in each of said identified industries by said brand monitoringplatform; sorting said acquired social media information by said brandmonitoring platform using a sorting interface provided by said brandmonitoring platform; and determining an audience score, determining anengagement score, generating an aggregate score, and determining saidsocial media strength of said brand and each of said competing brands insaid virtual social media environment by said brand monitoring platform;whereby said brand is benchmarked in comparison with said competingbrands based on said social media strength of said brand.
 2. Thecomputer implemented method of claim 1, wherein said dynamic generationof categories is based on an independent analysis of said acquiredsocial media information related to said brand and said competing brandsfrom each of said social media sources, and comprises: location of eachof said identified industries related to said brand and said each ofsaid competing brands; location of each of a plurality of authors ofsaid social media information; types of said social media sourcesutilized by said brand and said each of said competing brands; andmarketing elements.
 3. The computer implemented method of claim 2,wherein said independent analysis further comprises the step of:determining clusters of similar content portions from said acquiredsocial media information and identifying one or more common categoriesapplicable to said brand and said each of said competing brands in saideach of said identified industries from said determined clusters of saidsimilar content portions.
 4. The computer implemented method of claim 1,wherein said sorting said acquired social media information by saidbrand monitoring platform further comprises: acquiring inputs related tosaid brand and said competing brands in said each of said identifiedindustries; and sorting said acquired inputs into one or more of saiddynamically generated categories in said one or more hierarchical levelsusing said sorting interface.
 5. The computer implemented method ofclaim 1, wherein said determination of said audience score for saidbrand and said each of said competing brands by said brand monitoringplatform comprises: normalizing measures corresponding to each audiencescore metric parameter; assigning individual weights to said audiencescore metric parameters; determining a weighted average of saidnormalized measures corresponding to said each of said audience scoremetric parameters using said assigned individual weights; and measuringan aggregate reach of said brand and said each of said competing brandsin said virtual social media environment based on one or more of aplurality of said weighted audience score metric parameters using saidsorted social media information.
 6. The computer implemented method ofclaim 5, further comprising: normalizing measures corresponding to eachof said weighted audience score metric parameters by said brandmonitoring platform for reducing statistical differences between extremesaid measures corresponding to said each of said weighted audience scoreparameters.
 7. The computer implemented method of claim 5, wherein saidweighted audience score metric parameters comprise: number of followersof said brand and said each of said competing brands at said each ofsaid social media sources; rate of growth of said number of followers ofsaid brand and said each of said competing brands; number ofrecommendations for said brand and said each of said competing brands atsaid each of said social media sources from each of said followers;number of references made to said brand and said each of said competingbrands at said each of said social media sources by said followers; andaggregate responses to one or more of products, services, and eventsassociated with said brand and said each of said competing brands. 8.The computer implemented method of claim 1, wherein said determinationof said engagement score for said brand and said each of said competingbrands by said brand monitoring platform comprises: normalizing measurescorresponding to each engagement score metric parameter; assigningindividual weights to said engagement score metric parameters;determining a weighted average of said normalized measures correspondingto said each of said engagement score metric parameters using saidassigned individual weights; and measuring interaction between saidbrand and said each of said competing brands and said followers of saidbrand and said each of said competing brands by said brand monitoringplatform based on one or more of a plurality of said weighted engagementscore metric parameters using said sorted social media information. 9.The computer implemented method of claim 8, further comprising:normalizing measures corresponding to each of said weighted engagementscore metric parameters by said brand monitoring platform for reducingstatistical differences between extreme said measures corresponding tosaid each of said weighted engagement score parameters.
 10. The computerimplemented method of claim 8, wherein said weighted engagement scoremetric parameters comprise: nature of responses to one or more brandactions of said brand and said each of said competing brands from eachof said followers of said brand and said each of said competing brands;number of brand notification messages, sentiments of said followerstowards said brand and said each of said competing brands; number of fanposts extracted from said acquired social media information; andrelevance of said fan posts to said brand and said each of saidcompeting brands.
 11. The computer implemented method of claim 1,wherein said determination of said audience score and said engagementscore for said brand and said each of said competing brands by saidbrand monitoring platform further comprises: normalizing measurescorresponding to one or more of said audience score metric parametersand one or more of said engagement score metric parameters, based onsaid location of each of said identified industries related to saidbrand and said each of said competing brands, for reducing statisticaldifferences in said measures triggered by a difference of said locationof said each of said identified industries related to said brand andsaid each of said competing brands.
 12. The computer implemented methodof claim 1, further comprising: configuring one or more of said weightedaudience score metric parameters and one or more of said weightedengagement score metric parameters for said determination of saidaudience score and said engagement score respectively, by said brandmonitoring platform based on a predetermined criteria.
 13. The computerimplemented method of claim 1, wherein said brand monitoring platformgenerates said aggregate score for said brand and said each of saidcompeting brands by determining a weighted average of said determinedaudience score and said determined engagement score.
 14. The computerimplemented method of claim 1, wherein determining said social mediastrength of said brand in comparison with said competing brands by saidbrand monitoring platform comprises: assigning a rank to said brand andsaid each of said competing brands based on said generated aggregatescore; and determining said social media strength of said brand incomparison with said competing brands by comparing said assigned ranksof said brand and said each of said competing brands.
 15. A computerimplemented system for benchmarking a brand based on social mediastrength of said brand, comprising: a brand monitoring platformcomprising at least one processor configured to execute modules of saidbrand monitoring platform for monitoring said brand in a virtual socialmedia environment, said modules of said brand monitoring platformcomprising: an information acquisition module that acquires inputinformation on said brand; an industry identification module thatidentifies industries related to said brand and competing brands in saididentified industries using said acquired input information on saidbrand; said information acquisition module that acquires social mediainformation related to said brand and said competing brands in saididentified industries from a plurality of social media sources in saidvirtual social media environment via a network; a category generationmodule that dynamically generates categories in one or more hierarchicallevels in each of said identified industries; a sorting module thatsorts said acquired social media information using a sorting interface;and a scoring module that determines an audience score, determines anengagement score, generates an aggregate score, and determines saidsocial media strength of said brand and each of said competing brands;whereby said brand is benchmarked in comparison with said competingbrands based on said social media strength of said brand.
 16. Thecomputer implemented system of claim 15, wherein said dynamic generationof categories by said category generation module is based on anindependent analysis of said acquired social media information relatedto said brand and said competing brands from each of said social mediasources, and comprises: location of each of said identified industriesrelated to said brand and said each of said competing brands; locationof each of a plurality of authors of said social media information;types of said social media sources utilized by said brand and said eachof said competing brands; and marketing elements.
 17. The computerimplemented system of claim 16, wherein said independent analysis bysaid category generation module further comprises the step of:determining clusters of similar content portions from said acquiredsocial media information and identifying one or more common categoriesapplicable to said brand and said each of said competing brands in saideach of said identified industries from said determined clusters of saidsimilar content portions.
 18. The computer implemented system of claim15, wherein said sorting module further performs the steps of: acquiringinputs related to said brand and said competing brands in each of saididentified industries; and sorting said acquired inputs into one or moreof said dynamically generated categories in said one or morehierarchical levels using said sorting interface.
 19. The computerimplemented system of claim 15, wherein determination of said audiencescore for said brand and said each of said competing brands by saidscoring module comprises: normalizing measures corresponding to eachaudience score metric parameter; assigning individual weights to saidaudience score metric parameters; determining a weighted average of saidnormalized measures corresponding to said each of said audience scoremetric parameters using said assigned individual weights for saiddetermination of said audience score for said brand and said each ofsaid competing brands; and measuring an aggregate reach of said brandand said each of said competing brands in said virtual social mediaenvironment based on one or more of a plurality of said weightedaudience score metric parameters using said sorted social mediainformation.
 20. The computer implemented system of claim 19, whereinsaid scoring module further performs the step of: normalizing measurescorresponding to each of said weighted audience score metric parametersfor reducing statistical differences between extreme said measurescorresponding to said each of said weighted audience score metricparameters.
 21. The computer implemented system of claim 19, whereinsaid weighted audience score metric parameters comprise: number offollowers of said brand and said each of said competing brands at saideach of said social media sources; rate of growth of said number offollowers of said brand and said each of said competing brands; numberof recommendations for said brand and said each of said competing brandsat said each of said social media sources from each of said followers;number of references made to said brand and said each of said competingbrands at said each of said social media sources by said followers; andaggregate responses to one or more of products, services, and eventsassociated with said brand and said each of said competing brands. 22.The computer implemented system of claim 15, wherein said determinationof said engagement score for said brand and said each of said competingbrands by said scoring module comprises: normalizing measurescorresponding to each engagement score metric parameter; assigningindividual weights to said engagement score metric parameters;determining a weighted average of said normalized measures correspondingto said each of said engagement score metric parameters using saidassigned individual weights for said determination of said engagementscore for said brand and said each of said competing brands; andmeasuring interaction between said brand and said each of said competingbrands and said followers of said brand and said each of said competingbrands by said brand monitoring platform based on one or more of aplurality of weighted engagement score metric parameters using saidsorted social media information.
 23. The computer implemented system ofclaim 22, wherein said scoring module further performs the step of:normalizing measures corresponding to each of said weighted engagementscore metric parameters for reducing statistical differences betweenextreme said measures corresponding to said each of said weightedengagement score metric parameters.
 24. The computer implemented systemof claim 22, wherein said weighted engagement score metric parameterscomprise: nature of responses to one or more brand actions of said brandand said each of said competing brands from each of said followers ofsaid brand and said each of said competing brands; number of brandnotification messages, sentiments of said followers towards said brandand said each of said competing brands; number of fan posts extractedfrom said acquired social media information; and relevance of said fanposts to said brand and said each of said competing brands.
 25. Thecomputer implemented system of claim 15, wherein said scoring modulefurther performs the step of: normalizing measures corresponding to oneor more of said audience score metric parameters and one or more of saidengagement score metric parameters respectively, based on said locationof each of said identified industries related to said brand and saideach of said competing brands, for reducing statistical differences insaid measures triggered by a difference of said location of said each ofsaid identified industries related to said brand and said each of saidcompeting brands.
 26. The computer implemented system of claim 15,wherein said modules of said brand monitoring platform furthercomprises: a configuration module that configures one or more of saidweighted audience score metric parameters and one or more of saidweighted engagement score metric parameters for said determination ofsaid audience score and said engagement score respectively, based on apredetermined criteria.
 27. The computer implemented system of claim 15,wherein said scoring module generates said aggregate score for saidbrand and said each of said competing brands by determining a weightedaverage of said determined audience score and said determined engagementscore.
 28. The computer implemented system of claim 15, wherein saidscoring module further performs the steps of: assigning a rank to saidbrand and said each of said competing brands based on said generatedaggregate score; and determining said social media strength of saidbrand in comparison with said competing brands by comparing saidassigned ranks of said brand and said each of said competing brands. 29.A computer program product comprising a non-transitory computer readablestorage medium, said non-transitory computer readable storage mediumstoring computer program codes comprising instructions executable by atleast one processor, said computer program codes comprising: a firstcomputer program code for acquiring input information on a brand; asecond computer program code for identifying industries related to saidbrand and competing brands in said identified industries using saidacquired input information on said brand; a third computer program codefor acquiring social media information related to said brand and saidcompeting brands in said identified industries from a plurality ofsocial media sources in a virtual social media environment via anetwork; a fourth computer program code for dynamically generatingcategories in one or more hierarchical levels in each of said identifiedindustries based on an independent analysis of said acquired socialmedia information related to said brand and said competing brands fromeach of said social media sources; a fifth computer program code forsorting said acquired social media information related to said brand andsaid competing brands in said each of said identified industries intoone or more of said dynamically generated categories in said one or morehierarchical levels using a sorting interface; a sixth computer programcode for determining an audience score for said brand and each of saidcompeting brands by measuring an aggregate reach of said brand and saideach of said competing brands in said virtual social media environmentbased on one or more of a plurality of weighted audience score metricparameters using said sorted social media information; a seventhcomputer program code for determining an engagement score for said brandand said each of said competing brands by measuring interaction betweensaid brand and said each of said competing brands and their followersbased on one or more of a plurality of weighted engagement score metricparameters using said sorted social media information; an eighthcomputer program code for generating an aggregate score for said brandand said each of said competing brands using said determined audiencescore and said determined engagement score; said eighth computer programcode further assigning a rank to said brand and said each of saidcompeting brands based on said generated aggregate score; and saideighth computer program code further determining social media strengthof said brand in comparison with said competing brands in said virtualsocial media environment by comparing said assigned ranks of said brandand said each of said competing brands.